From 52dc5d8afd4761e543ad18a2fe3127991f09b4f4 Mon Sep 17 00:00:00 2001
From: Ayush <ayush@chaurarsia@gmail.com>
Date: Wed, 24 Jan 2024 01:10:30 +0530
Subject: [PATCH] fix product recommender

---
 examples/product-recommender/main.ipynb | 5445 ++++++++++++-----------
 1 file changed, 2904 insertions(+), 2541 deletions(-)

diff --git a/examples/product-recommender/main.ipynb b/examples/product-recommender/main.ipynb
index 53c4d96f..7c631fae 100644
--- a/examples/product-recommender/main.ipynb
+++ b/examples/product-recommender/main.ipynb
@@ -1,2595 +1,2958 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "YmdWGrw4t5G2"
-   },
-   "source": [
-    "# Product Recommender using Collaborative Filtering and LanceDB\n",
-    "\n",
-    "We are going to use **LanceDB** and **Collaborative Filtering** to recommend products based on a user's past buying history. We used the <a href=\"https://www.kaggle.com/datasets/yasserh/instacart-online-grocery-basket-analysis-dataset\">**Instacart dataset**</a> as our data for this example.\n",
-    "\n",
-    "![picture](https://daxg39y63pxwu.cloudfront.net/images/blog/product-recommendation-system-projects/Product_Recommendation_System_Project_Ideas_and_Examples.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "lXd46ecEt5G7"
-   },
-   "source": [
-    "To run this example, you must first create a Kaggle account. Then, go to the 'Account' tab of your user profile and select 'Create New Token'. This will trigger the download of kaggle.json, a file containing your API credentials.\n",
-    "\n",
-    "Add Kaggle credentials to `~/.kaggle/kaggle.json` on Linux, OSX, and other UNIX-based operating systems or `C:\\Users\\<Windows-username>\\.kaggle\\kaggle.json` for Window's users.\n",
-    "\n",
-    "In Google Colab, run the snippet below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "l6TTPIF_omEy"
-   },
-   "outputs": [],
-   "source": [
-    "import json\n",
-    "\n",
-    "with open('/root/.kaggle/kaggle.json', 'w') as fp:\n",
-    "    fp.write(json.dumps({\"username\":\"YOUR_USERNAME\",\"key\":\"YOUR_SECRET_KEY\"}))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Install dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YmdWGrw4t5G2"
+      },
+      "source": [
+        "# Product Recommender using Collaborative Filtering and LanceDB\n",
+        "\n",
+        "We are going to use **LanceDB** and **Collaborative Filtering** to recommend products based on a user's past buying history. We used the <a href=\"https://www.kaggle.com/datasets/yasserh/instacart-online-grocery-basket-analysis-dataset\">**Instacart dataset**</a> as our data for this example.\n",
+        "\n",
+        "![picture](https://daxg39y63pxwu.cloudfront.net/images/blog/product-recommendation-system-projects/Product_Recommendation_System_Project_Ideas_and_Examples.png)"
+      ]
     },
-    "id": "R3_Hq2VC4_zT",
-    "outputId": "f55d20d1-9953-457c-f41b-d09912b06188"
-   },
-   "outputs": [],
-   "source": [
-    "!pip install numpy pandas scipy kaggle implicit torch lancedb"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "i_eatRhaIGIz"
-   },
-   "source": [
-    "### Importing libraries"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lXd46ecEt5G7"
+      },
+      "source": [
+        "To run this example, you must first create a Kaggle account. Then, go to the 'Account' tab of your user profile and select 'Create New Token'. This will trigger the download of kaggle.json, a file containing your API credentials.\n",
+        "\n",
+        "Add Kaggle credentials to `~/.kaggle/kaggle.json` on Linux, OSX, and other UNIX-based operating systems or `C:\\Users\\<Windows-username>\\.kaggle\\kaggle.json` for Window's users.\n",
+        "\n",
+        "In Google Colab, run the snippet below."
+      ]
     },
-    "id": "emp_MSXZt5G8",
-    "outputId": "b9719d21-25a6-461e-d571-164f04e599d0"
-   },
-   "outputs": [],
-   "source": [
-    "import zipfile\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import scipy.sparse\n",
-    "import torch\n",
-    "import implicit\n",
-    "from implicit import evaluation\n",
-    "import pydantic\n",
-    "import lancedb\n",
-    "from lancedb.pydantic import pydantic_to_schema, vector"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "bUGkN85V4_zY"
-   },
-   "source": [
-    "### Load the dataset\n",
-    "Now we can download the dataset. You will need to accept the rules of the `instacart-market-basket-analysis` competition, which you can do so [here](https://www.kaggle.com/competitions/instacart-market-basket-analysis/rules)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "l6TTPIF_omEy",
+        "outputId": "d2cf1685-103e-4b62-bae3-a16d171a928f",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Kaggle API key file created and moved successfully.\n"
+          ]
+        }
+      ],
+      "source": [
+        "import json\n",
+        "import os\n",
+        "\n",
+        "# Set the file path\n",
+        "kaggle_json_path = '/content/kaggle.json'\n",
+        "\n",
+        "# Write Kaggle API key to the file\n",
+        "with open(kaggle_json_path, 'w') as fp:\n",
+        "    json.dump({\"username\": \"\", \"key\": \"\"}, fp)\n",
+        "\n",
+        "# Move the file to the correct location\n",
+        "os.system('mkdir -p ~/.kaggle')\n",
+        "os.system(f'mv {kaggle_json_path} ~/.kaggle/kaggle.json')\n",
+        "\n",
+        "# Set permissions\n",
+        "os.system('chmod 600 ~/.kaggle/kaggle.json')\n",
+        "\n",
+        "print(\"Kaggle API key file created and moved successfully.\")"
+      ]
     },
-    "id": "09gdQyBu4_zY",
-    "outputId": "4a68ac04-2c9f-4a11-a7da-f24e34270256"
-   },
-   "outputs": [],
-   "source": [
-    "!kaggle competitions download -c instacart-market-basket-analysis"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "K4Q4cOX-4_zY"
-   },
-   "source": [
-    "We must now extract the zip files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "f3g296nL4_zZ"
-   },
-   "outputs": [],
-   "source": [
-    "files = [\n",
-    "    'instacart-market-basket-analysis.zip',\n",
-    "    'order_products__train.csv.zip',\n",
-    "    'order_products__prior.csv.zip',\n",
-    "    'products.csv.zip',\n",
-    "    'orders.csv.zip'\n",
-    "]\n",
-    "\n",
-    "for filename in files:\n",
-    "    with zipfile.ZipFile(filename, 'r') as zip_ref:\n",
-    "        zip_ref.extractall('./')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "oLgkRIfq4_zZ"
-   },
-   "source": [
-    "Now we can move on to loading the dataset. We'll first read the csv files and create dataframes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "cBbbR7Rut5G_"
-   },
-   "outputs": [],
-   "source": [
-    "products = pd.read_csv('products.csv')\n",
-    "orders = pd.read_csv('orders.csv')\n",
-    "order_products = pd.concat([pd.read_csv('order_products__train.csv'), pd.read_csv('order_products__prior.csv')])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "5FV_GGjst5HA"
-   },
-   "source": [
-    "Since there isn't a user rating attribute, we'll gather \"confidence\" data by looking at the frequency of each item purchased by a user, and store this in the `data` dataframe."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data Manipulation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ZjRh7RYpt5HB"
-   },
-   "outputs": [],
-   "source": [
-    "customer_order_products = pd.merge(orders, order_products, how='inner',on='order_id')\n",
-    "\n",
-    "# create confidence table\n",
-    "data = customer_order_products.groupby(['user_id', 'product_id'])[['order_id']].count().reset_index()\n",
-    "data.columns=[\"user_id\", \"product_id\", \"total_orders\"]\n",
-    "data.product_id = data.product_id.astype('int64')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "77lvwm0St5HC"
-   },
-   "source": [
-    "Let's create a couple of test users to examine the recommendations later:\n",
-    "- 1st test user: buys 50 sodas: **Zero Calorie Cola**\n",
-    "- 2nd test user: buys organic produce: **Organic Whole Milk** and **Organic Blackberries**"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 206
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c6G45HrUqNx5"
+      },
+      "source": [
+        "### Install dependencies"
+      ]
     },
-    "id": "A06EfAf-t5HC",
-    "outputId": "135aa843-f46a-4fef-d2ae-ddfc00512ad2"
-   },
-   "outputs": [
     {
-     "data": {
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-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
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-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
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-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
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-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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-       "        const element = document.querySelector('#df-402acb6b-a1c8-474b-ad8a-edee1973e367');\n",
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-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
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-       "\n",
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-       "  .colab-df-quickchart {\n",
-       "    background-color: #E8F0FE;\n",
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-       "    border-radius: 50%;\n",
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-       "    height: 32px;\n",
-       "    padding: 0 0 0 0;\n",
-       "    width: 32px;\n",
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-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: #E2EBFA;\n",
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-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
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-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart:hover {\n",
-       "    background-color: #434B5C;\n",
-       "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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-       "\n",
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-       "    async function quickchart(key) {\n",
-       "      const charts = await google.colab.kernel.invokeFunction(\n",
-       "          'suggestCharts', [key], {});\n",
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-       "      quickchartButtonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R3_Hq2VC4_zT",
+        "outputId": "ee47bbd5-d1c3-4900-894e-2530190e17e7"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (1.23.5)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (1.5.3)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (1.11.4)\n",
+            "Requirement already satisfied: kaggle in /usr/local/lib/python3.10/dist-packages (1.5.16)\n",
+            "Collecting implicit\n",
+            "  Downloading implicit-0.7.2-cp310-cp310-manylinux2014_x86_64.whl (8.9 MB)\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.9/8.9 MB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.1.0+cu121)\n",
+            "Collecting lancedb\n",
+            "  Downloading lancedb-0.5.0-py3-none-any.whl (87 kB)\n",
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+            "\u001b[?25hCollecting ratelimiter~=1.0 (from lancedb)\n",
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+            "Installing collected packages: ratelimiter, semver, pyarrow, py, overrides, deprecation, retry, pylance, implicit, lancedb\n",
+            "  Attempting uninstall: pyarrow\n",
+            "    Found existing installation: pyarrow 10.0.1\n",
+            "    Uninstalling pyarrow-10.0.1:\n",
+            "      Successfully uninstalled pyarrow-10.0.1\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0mSuccessfully installed deprecation-2.1.0 implicit-0.7.2 lancedb-0.5.0 overrides-7.6.0 py-1.11.0 pyarrow-15.0.0 pylance-0.9.6 ratelimiter-1.2.0.post0 retry-0.9.2 semver-3.0.2\n"
+          ]
+        }
       ],
-      "text/plain": [
-       "          user_id  product_id  total_orders\n",
-       "13863744   206209       48697             1\n",
-       "13863745   206209       48742             2\n",
-       "13863746   206210       46149            50\n",
-       "13863747   206211       27845            49\n",
-       "13863748   206211       26604            32"
+      "source": [
+        "!pip install numpy pandas scipy kaggle implicit torch lancedb"
       ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "data_new = pd.DataFrame([[data.user_id.max() + 1, 46149, 50],\n",
-    "                         [data.user_id.max() + 2, 27845, 49],\n",
-    "                         [data.user_id.max() + 2, 26604, 32]\n",
-    "                        ], columns=['user_id', 'product_id', 'total_orders'])\n",
-    "data = pd.concat([data, data_new]).reset_index(drop = True)\n",
-    "data.tail()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "xBC-8PFTt5HD"
-   },
-   "source": [
-    "In the next step, we will extract user and product unique ids, in order to create a `CSR (Compressed Sparse Row)` matrix. This will allow us to perform collaborative filtering.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "v2_2R7zmt5HE"
-   },
-   "outputs": [],
-   "source": [
-    "# extract unique user and product ids\n",
-    "unique_users = list(np.sort(data.user_id.unique()))\n",
-    "unique_products = list(np.sort(products.product_id.unique()))\n",
-    "purchases = list(data.total_orders)\n",
-    "\n",
-    "# create zero-based index position <-> user/item ID mappings\n",
-    "index_to_user = pd.Series(unique_users)\n",
-    "\n",
-    "# create reverse mappings from user/item ID to index positions\n",
-    "user_to_index = pd.Series(data=index_to_user.index + 1, index=index_to_user.values)\n",
-    "\n",
-    "# create row and column for user and product ids\n",
-    "users_rows = data.user_id.astype(int)\n",
-    "products_cols = data.product_id.astype(int)\n",
-    "\n",
-    "# create CSR matrix\n",
-    "matrix = scipy.sparse.csr_matrix((purchases, (users_rows, products_cols)), shape=(len(unique_users) + 1, len(unique_products) + 1))\n",
-    "matrix.data = np.nan_to_num(matrix.data, copy=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "II6wOH96t5HF"
-   },
-   "source": [
-    "Let's now create a recommender model using the **implicit** library. The recommendation model is based off the algorithms described in the paper [Collaborative Filtering for Implicit Feedback Datasets](https://www.researchgate.net/publication/220765111_Collaborative_Filtering_for_Implicit_Feedback_Datasets) with performance optimizations described in [Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.379.6473&rep=rep1&type=pdf).\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Difference between colloborative and content filtering\n",
-    "\n",
-    "![picture](https://miro.medium.com/v2/resize:fit:1400/0*R8qw_CXxCc4600bQ.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 49,
-     "referenced_widgets": [
-      "1a0967f7267a4774ad99cedcb45f4cfc",
-      "ae54a8d76aca4327a938dda5c93c78ca",
-      "be5ee6d5b76b4deab6e84fdc8931b899",
-      "7e17ec4dd0484ac1b2a0ace262b6b518",
-      "ac143cc6e1394eb6963d1e6995d09447",
-      "5e66de2e9882465b8ba1f252f049785a",
-      "4cf95f3891134632b14c606c744fded3",
-      "734cebd21ff5403891fe799826d1d98b",
-      "aafb9c85a5b74f18bbbc41fc3132dd1e",
-      "261d4713dfd040318b5f542609a05129",
-      "6ca9eb350a6b4a1b8bfdb500ce00c0c4"
-     ]
     },
-    "id": "k0GW99kxt5HF",
-    "outputId": "3d8e7947-bdfe-460a-a8bc-8f32631aaa36"
-   },
-   "outputs": [],
-   "source": [
-    "#split data into train and test splits\n",
-    "train, test = evaluation.train_test_split(matrix, train_percentage=0.9)\n",
-    "\n",
-    "# initialize the recommender model\n",
-    "model = implicit.als.AlternatingLeastSquares(factors=128,\n",
-    "                                             regularization=0.05,\n",
-    "                                             iterations=50,\n",
-    "                                             num_threads=1)\n",
-    "\n",
-    "alpha = 15\n",
-    "train = (train * alpha).astype('double')\n",
-    "\n",
-    "# train the model on CSR matrix\n",
-    "model.fit(train, show_progress = True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "yN80hSojt5HF"
-   },
-   "source": [
-    "## Let's now evaluate the model."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 120,
-     "referenced_widgets": [
-      "9bff71b52a854ba788601810574f7689",
-      "1083778f7a0941e18a436cc7c6cddda8",
-      "6336bd02113245f7883a48961d80ed55",
-      "bffd8e57b04946ae9289af62a52b9fc8",
-      "03397ba7fce7452593eddb4fa4fd27f4",
-      "ccd7286cfb904ad8a5486738357a6d39",
-      "d157f99a712c43cdb7a26c6470d0e615",
-      "c9110b853367476a8743eea0c522a265",
-      "4ae829ed16f846edade21e0730ff6f37",
-      "95e65e6bceed40db9cea2a0adffed3fb",
-      "cbff0accbd0f492597900c93a340d59f"
-     ]
-    },
-    "id": "BbD8of_nt5HG",
-    "outputId": "91ce36fd-f569-41c9-859e-71cf9709f8d4"
-   },
-   "outputs": [],
-   "source": [
-    "test = (test * alpha).astype('double')\n",
-    "evaluation.ranking_metrics_at_k(model, train, test, K=100,\n",
-    "                         show_progress=True, num_threads=1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "LNmva3Dlt5HG"
-   },
-   "source": [
-    "From the model, we'll be able to retrieve item and user factors, which we can use later on to store in LanceDB as vector embeddings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i_eatRhaIGIz"
+      },
+      "source": [
+        "### Importing libraries"
+      ]
     },
-    "id": "JUtCROQKt5HG",
-    "outputId": "38c55345-deec-4555-f9fd-33d88742b8de"
-   },
-   "outputs": [
     {
-     "data": {
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-       "   2.28196708e-03 -1.67180516e-03 -1.01774244e-03 -1.60270859e-03\n",
-       "  -8.59596417e-04 -1.55605783e-04  5.09508187e-04 -1.87147816e-03\n",
-       "   1.08829510e-04  9.37477977e-04  1.45089245e-04  1.11689174e-03\n",
-       "   1.42429711e-03  2.01329254e-04  2.59526510e-04 -5.31102007e-04\n",
-       "   7.97511311e-05 -8.36311374e-04  2.43162527e-03  1.07298889e-04\n",
-       "   3.93533992e-04  3.79959791e-04 -2.30675470e-03  9.03452164e-04\n",
-       "  -1.05375238e-03 -1.16557023e-03 -2.24862527e-03 -8.47899537e-06\n",
-       "   3.58379795e-03 -1.97861204e-03  7.35816080e-04  2.40693684e-03\n",
-       "   4.44425794e-04  1.32714782e-03  2.48055498e-04 -2.01600906e-03\n",
-       "   3.14348348e-04  2.91592046e-03 -4.29794611e-03  8.71473225e-04\n",
-       "  -4.66790429e-04 -2.59004976e-03  3.57797777e-04 -5.13199251e-04\n",
-       "  -3.72246868e-04 -4.57473885e-04 -1.13439921e-03 -5.64167567e-04\n",
-       "   4.08002222e-03  1.71154004e-03 -1.18066662e-03  1.10202690e-03\n",
-       "  -9.25508677e-04  9.13127908e-04 -3.54050135e-04 -1.86034129e-03\n",
-       "   6.16788166e-04 -1.34601502e-03 -6.19047554e-04  6.99885830e-04]])"
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "emp_MSXZt5G8"
+      },
+      "outputs": [],
+      "source": [
+        "import zipfile\n",
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "import scipy.sparse\n",
+        "import torch\n",
+        "import implicit\n",
+        "from implicit import evaluation\n",
+        "import pydantic\n",
+        "import lancedb\n",
+        "from lancedb.pydantic import pydantic_to_schema, vector"
       ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.item_factors[1:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/"
     },
-    "id": "O3onbJmnt5HG",
-    "outputId": "988d73ee-a007-4848-d055-bc5b34f8900b"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "Matrix([[ 1.76306021e+00  5.84278941e-01 -1.31811535e+00  2.58672982e-02\n",
-       "  -1.47269890e-01 -1.99104631e+00  2.27232277e-01 -1.44048560e+00\n",
-       "   6.22047544e-01 -6.87879205e-01 -4.23879363e-02 -1.50391304e+00\n",
-       "  -1.04289226e-01 -9.02593374e-01  6.43032670e-01 -3.58335793e-01\n",
-       "  -6.54706135e-02  8.50856245e-01  7.79175341e-01 -4.51985866e-01\n",
-       "   8.55366886e-01  1.36438921e-01 -5.49016356e-01  4.83980298e-01\n",
-       "  -1.40851259e-01 -5.33492684e-01  5.68639338e-01  1.45152867e-01\n",
-       "   1.76261580e+00  5.22969246e-01 -2.21816874e+00  1.65968144e+00\n",
-       "  -8.83751035e-01  7.76956260e-01  1.25151992e+00 -3.25308472e-01\n",
-       "  -1.49347281e+00 -1.01729310e+00 -4.59959418e-01  7.20718205e-01\n",
-       "  -7.15589583e-01  6.45604208e-02 -8.51610005e-01  3.01664054e-01\n",
-       "  -4.82483774e-01 -1.79249153e-01 -1.40011147e-01 -4.23951089e-01\n",
-       "  -1.13460481e+00  1.13597369e+00 -7.75141537e-01  1.12328935e+00\n",
-       "   8.09678361e-02  3.74672678e-03 -5.49308121e-01  3.00086081e-01\n",
-       "  -3.05186462e+00  8.97184253e-01  3.36005628e-01 -9.53863025e-01\n",
-       "  -1.40538502e+00  8.67709160e-01 -1.28687751e+00  2.50722909e+00\n",
-       "  -1.39235985e+00  8.41415584e-01 -3.63795489e-01 -4.97452825e-01\n",
-       "   2.82425970e-01 -1.41166592e+00  1.87965715e+00  2.28982329e+00\n",
-       "   2.65981466e-01  1.40662909e+00 -5.27400672e-01  2.24494290e+00\n",
-       "  -1.02008140e+00  9.12805080e-01 -1.06372535e+00 -8.54292572e-01\n",
-       "   7.36877501e-01  2.80537099e-01 -1.14187610e+00  3.33846539e-01\n",
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-       "  -1.62952110e-01  1.63383838e-02 -2.03721905e+00  1.65726721e+00\n",
-       "   1.24682081e+00  1.13641596e+00 -1.21482491e+00  1.33682203e+00\n",
-       "   1.25738907e+00  1.26730061e+00  4.19435412e-01  5.83988726e-01\n",
-       "  -8.63369167e-01 -2.17941441e-02 -9.48838413e-01 -1.69335604e-01\n",
-       "  -7.81806767e-01  1.10969472e+00 -3.43311757e-01  7.97569871e-01\n",
-       "   7.03379154e-01  5.70429564e-02 -1.25305027e-01  7.07163095e-01\n",
-       "  -5.44186294e-01  1.48075533e+00 -4.71153051e-01  5.30716360e-01\n",
-       "  -3.40850413e-01 -6.89384997e-01  3.17025274e-01 -8.00609946e-01\n",
-       "  -1.77810180e+00  7.57392883e-01 -8.67088586e-02 -1.57348573e+00\n",
-       "  -1.63898838e+00  5.90374112e-01 -9.29109752e-02 -1.37256697e-01]\n",
-       " [ 3.68525267e+00  2.21822786e+00 -3.72133762e-01 -9.38002110e-01\n",
-       "  -1.82598040e-01 -1.57692325e+00 -1.60580468e+00  7.22465396e-01\n",
-       "  -1.39234257e+00 -1.21537292e+00 -2.97544384e+00 -1.43866599e+00\n",
-       "   2.61079460e-01 -7.03472435e-01  4.57634866e-01 -9.56056535e-01\n",
-       "   1.93661535e+00 -1.24941671e+00 -2.09334850e+00  1.10176265e+00\n",
-       "   2.63000101e-01  5.81728339e-01  7.04845428e-01 -3.02078456e-01\n",
-       "   1.77111065e+00  1.04489577e+00  1.51791990e+00  2.20976304e-02\n",
-       "  -7.39717960e-01 -6.22645319e-01 -1.56369075e-01  1.04076886e+00\n",
-       "   1.25483823e+00 -3.32873642e-01  4.33149636e-01 -1.22933829e+00\n",
-       "  -7.20005214e-01  5.78506827e-01  1.00301087e+00  8.72102618e-01\n",
-       "   2.50873059e-01  8.88309538e-01 -2.71842957e-01 -9.80645061e-01\n",
-       "   1.66997731e+00 -1.29030275e+00  1.46061301e-01  3.29237163e-01\n",
-       "  -2.51785922e+00 -3.69922400e-01  4.70215261e-01  2.95814991e+00\n",
-       "   7.99638331e-01 -7.45547190e-02  1.54420769e+00  1.31914115e+00\n",
-       "  -5.89660287e-01 -9.22154129e-01 -1.61234534e+00 -1.25994372e+00\n",
-       "   1.22705674e+00  7.55788326e-01 -9.14623439e-01 -1.08628643e+00\n",
-       "   1.54585168e-01 -6.50843084e-01  7.14638084e-02  2.60113406e+00\n",
-       "  -3.26147604e+00 -7.06203341e-01 -1.01394987e+00  1.81137729e+00\n",
-       "  -1.33361208e+00  6.01522505e-01 -2.62121201e+00  1.60872042e+00\n",
-       "   1.40612996e+00 -2.63518363e-01  2.49749565e+00  7.56558836e-01\n",
-       "   9.12506133e-03  4.42859173e-01  3.19830328e-01 -1.19115925e+00\n",
-       "  -3.87303567e+00  4.34802413e-01 -1.38765180e+00  1.02910614e+00\n",
-       "  -6.26348317e-01  8.68485034e-01 -1.63119793e+00  6.86433792e-01\n",
-       "   4.61489171e-01 -1.19542277e+00  3.53666723e-01 -7.63388455e-01\n",
-       "   1.00943439e-01  2.46762371e+00  3.33791876e+00  4.14971447e+00\n",
-       "  -1.56401730e+00  5.20050287e-01 -1.22738254e+00  5.72006464e-01\n",
-       "   7.93922722e-01  1.36654353e+00  1.30395845e-01 -1.13550574e-01\n",
-       "  -1.27993798e+00  1.18952966e+00 -2.28072000e+00 -1.86128521e+00\n",
-       "  -1.75689876e+00  2.94097590e+00 -1.61757767e-01  6.65994763e-01\n",
-       "  -2.79640174e+00 -7.66005337e-01  1.12821102e-01 -3.13482106e-01\n",
-       "   5.64184427e-01 -2.94495314e-01  1.34794497e+00 -1.06749706e-01\n",
-       "   2.95547748e+00  7.01298356e-01  2.26510024e+00  3.02275372e+00]])"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bUGkN85V4_zY"
+      },
+      "source": [
+        "### Load the dataset\n",
+        "Now we can download the dataset. You will need to accept the rules of the `instacart-market-basket-analysis` competition, which you can do so [here](https://www.kaggle.com/competitions/instacart-market-basket-analysis/rules)."
       ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "model.user_factors[1:3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "38rssdYCBR4E"
-   },
-   "source": [
-    "## Let's save the data and create a empty LanceDB Table using a Pydantic model.\n",
-    "A Table is designed to store large numbers of columns and huge quantities of data! For those interested, a LanceDB is columnar-based, and uses Lance, an open data format to store data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "3_ykVLT6t5HH"
-   },
-   "outputs": [],
-   "source": [
-    "db = lancedb.connect(\"data/lancedb\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "ufHsF0o4t5HI"
-   },
-   "outputs": [],
-   "source": [
-    "class ProductModel(pydantic.BaseModel):\n",
-    "    product_id: int\n",
-    "    product_name: str\n",
-    "    vector: vector(128)\n",
-    "schema = pydantic_to_schema(ProductModel)\n",
-    "table_name = 'product_recommender'\n",
-    "tbl = db.create_table(table_name, schema=schema, mode=\"overwrite\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "0-2K-g4-t5HJ"
-   },
-   "source": [
-    "Let's now store our item factors into the table via the vector column of `product_entries`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "NOOPF9zOt5HJ"
-   },
-   "outputs": [],
-   "source": [
-    "# Transform items into factors\n",
-    "items_factors = model.item_factors\n",
-    "product_entries = products[['product_id', 'product_name']].drop_duplicates()\n",
-    "product_entries['product_id'] = product_entries.product_id.astype('int64')\n",
-    "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "item_embeddings = items_factors[1:].to_numpy().tolist() if device == \"cuda\" else items_factors[1:].tolist()\n",
-    "product_entries['vector'] = item_embeddings\n",
-    "\n",
-    "tbl.add(product_entries)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "j3aU4z-tSbWE"
-   },
-   "source": [
-    "## Let's create an ANN index in order to speed up retrieval. This might take a while."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "H8HyvjCFSeaz"
-   },
-   "outputs": [],
-   "source": [
-    "tbl.create_index(num_partitions=256, num_sub_vectors=16)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ibNMrxyRt5HK"
-   },
-   "source": [
-    "This is a helper method for analysing recommendations later.\n",
-    "This method returns top N products that someone bought in the past (based on product quantity)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Uzgk5Od0t5HK"
-   },
-   "outputs": [],
-   "source": [
-    "def products_bought_by_user_in_the_past(user_id: int, top: int = 10):\n",
-    "\n",
-    "    selected = data[data.user_id == user_id].sort_values(by=['total_orders'], ascending=False)\n",
-    "\n",
-    "    selected['product_name'] = selected['product_id'].map(product_entries.set_index('product_id')['product_name'])\n",
-    "    selected = selected[['product_id', 'product_name', 'total_orders']].reset_index(drop=True)\n",
-    "    if selected.shape[0] < top:\n",
-    "        return selected\n",
-    "\n",
-    "    return selected[:top]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "ULyVnHEXt5HK"
-   },
-   "source": [
-    "Let's retrieve our test users so we can query for recommendations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "id": "Wwl7yFKTt5HK"
-   },
-   "outputs": [],
-   "source": [
-    "test_user_ids = [206210, 206211]\n",
-    "test_user_factors = model.user_factors[user_to_index[test_user_ids]]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "id": "wTh61ou3t5HL"
-   },
-   "source": [
-    "## Let's now query LanceDB to retrieve recommendations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 868
     },
-    "id": "UiZg4Iset5HL",
-    "outputId": "797e1876-b943-48c9-eced-a595011ca33a"
-   },
-   "outputs": [
     {
-     "data": {
-      "text/html": [
-       "\n",
-       "  <div id=\"df-5d4c75f0-45e0-4a25-a2df-18786d1c3afb\" class=\"colab-df-container\">\n",
-       "    <div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>product_id</th>\n",
-       "      <th>product_name</th>\n",
-       "      <th>vector</th>\n",
-       "      <th>_distance</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>46149</td>\n",
-       "      <td>Zero Calorie Cola</td>\n",
-       "      <td>[0.0022252752, 0.006192103, -0.030976068, -0.0...</td>\n",
-       "      <td>42.362167</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>196</td>\n",
-       "      <td>Soda</td>\n",
-       "      <td>[-0.026970126, -0.018141357, -0.058909897, -0....</td>\n",
-       "      <td>42.566917</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>40939</td>\n",
-       "      <td>Drinking Water</td>\n",
-       "      <td>[-0.0022220984, 0.0038040192, -0.014334261, -0...</td>\n",
-       "      <td>42.823524</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>41400</td>\n",
-       "      <td>Crunchy Oats 'n Honey Granola Bars</td>\n",
-       "      <td>[0.00020495932, 0.01123721, -0.017992454, 0.00...</td>\n",
-       "      <td>42.825871</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>37710</td>\n",
-       "      <td>Trail Mix</td>\n",
-       "      <td>[-0.00409454, 0.013049758, -0.015458386, 0.002...</td>\n",
-       "      <td>42.887043</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>5</th>\n",
-       "      <td>46061</td>\n",
-       "      <td>Popcorn</td>\n",
-       "      <td>[-0.0024577982, 0.0062640505, 0.0007137757, -0...</td>\n",
-       "      <td>42.894321</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>6</th>\n",
-       "      <td>38928</td>\n",
-       "      <td>0% Greek Strained Yogurt</td>\n",
-       "      <td>[0.0039202925, -0.0039743707, -0.012298337, 0....</td>\n",
-       "      <td>42.912727</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>7</th>\n",
-       "      <td>31651</td>\n",
-       "      <td>Extra Fancy Unsalted Mixed Nuts</td>\n",
-       "      <td>[0.0035735483, -0.006829423, -0.009457169, 0.0...</td>\n",
-       "      <td>42.922546</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>8</th>\n",
-       "      <td>22802</td>\n",
-       "      <td>Mineral Water</td>\n",
-       "      <td>[0.02247884, 0.003889028, -0.020661984, -0.031...</td>\n",
-       "      <td>42.935951</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>39657</td>\n",
-       "      <td>Milk Chocolate Almonds</td>\n",
-       "      <td>[0.00749532, 0.00577313, -0.016842585, -0.0015...</td>\n",
-       "      <td>42.946739</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>\n",
-       "    <div class=\"colab-df-buttons\">\n",
-       "\n",
-       "  <div class=\"colab-df-container\">\n",
-       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d4c75f0-45e0-4a25-a2df-18786d1c3afb')\"\n",
-       "            title=\"Convert this dataframe to an interactive table.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
-       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
-       "  </svg>\n",
-       "    </button>\n",
-       "\n",
-       "  <style>\n",
-       "    .colab-df-container {\n",
-       "      display:flex;\n",
-       "      gap: 12px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert {\n",
-       "      background-color: #E8F0FE;\n",
-       "      border: none;\n",
-       "      border-radius: 50%;\n",
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-       "      display: none;\n",
-       "      fill: #1967D2;\n",
-       "      height: 32px;\n",
-       "      padding: 0 0 0 0;\n",
-       "      width: 32px;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-convert:hover {\n",
-       "      background-color: #E2EBFA;\n",
-       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "      fill: #174EA6;\n",
-       "    }\n",
-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-5d4c75f0-45e0-4a25-a2df-18786d1c3afb button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-5d4c75f0-45e0-4a25-a2df-18786d1c3afb');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
-       "        const docLink = document.createElement('div');\n",
-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
-       "      }\n",
-       "    </script>\n",
-       "  </div>\n",
-       "\n",
-       "\n",
-       "<div id=\"df-ea560213-31e9-46f2-a27a-e47ef8353b8d\">\n",
-       "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ea560213-31e9-46f2-a27a-e47ef8353b8d')\"\n",
-       "            title=\"Suggest charts.\"\n",
-       "            style=\"display:none;\">\n",
-       "\n",
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
-       "     width=\"24px\">\n",
-       "    <g>\n",
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-       "    </g>\n",
-       "</svg>\n",
-       "  </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: #E8F0FE;\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
-       "    cursor: pointer;\n",
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-       "    height: 32px;\n",
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-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: #E2EBFA;\n",
-       "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: #174EA6;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "    background-color: #3B4455;\n",
-       "    fill: #D2E3FC;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart:hover {\n",
-       "    background-color: #434B5C;\n",
-       "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "    fill: #FFFFFF;\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "  <script>\n",
-       "    async function quickchart(key) {\n",
-       "      const charts = await google.colab.kernel.invokeFunction(\n",
-       "          'suggestCharts', [key], {});\n",
-       "    }\n",
-       "    (() => {\n",
-       "      let quickchartButtonEl =\n",
-       "        document.querySelector('#df-ea560213-31e9-46f2-a27a-e47ef8353b8d button');\n",
-       "      quickchartButtonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "09gdQyBu4_zY",
+        "outputId": "bb92fb9e-df75-47a5-b50d-290ed0555ef4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Downloading instacart-market-basket-analysis.zip to /content\n",
+            " 92% 181M/196M [00:01<00:00, 81.3MB/s]\n",
+            "100% 196M/196M [00:01<00:00, 105MB/s] \n"
+          ]
+        }
       ],
-      "text/plain": [
-       "   product_id                        product_name  \\\n",
-       "0       46149                   Zero Calorie Cola   \n",
-       "1         196                                Soda   \n",
-       "2       40939                      Drinking Water   \n",
-       "3       41400  Crunchy Oats 'n Honey Granola Bars   \n",
-       "4       37710                           Trail Mix   \n",
-       "5       46061                             Popcorn   \n",
-       "6       38928            0% Greek Strained Yogurt   \n",
-       "7       31651     Extra Fancy Unsalted Mixed Nuts   \n",
-       "8       22802                       Mineral Water   \n",
-       "9       39657              Milk Chocolate Almonds   \n",
-       "\n",
-       "                                              vector  _distance  \n",
-       "0  [0.0022252752, 0.006192103, -0.030976068, -0.0...  42.362167  \n",
-       "1  [-0.026970126, -0.018141357, -0.058909897, -0....  42.566917  \n",
-       "2  [-0.0022220984, 0.0038040192, -0.014334261, -0...  42.823524  \n",
-       "3  [0.00020495932, 0.01123721, -0.017992454, 0.00...  42.825871  \n",
-       "4  [-0.00409454, 0.013049758, -0.015458386, 0.002...  42.887043  \n",
-       "5  [-0.0024577982, 0.0062640505, 0.0007137757, -0...  42.894321  \n",
-       "6  [0.0039202925, -0.0039743707, -0.012298337, 0....  42.912727  \n",
-       "7  [0.0035735483, -0.006829423, -0.009457169, 0.0...  42.922546  \n",
-       "8  [0.02247884, 0.003889028, -0.020661984, -0.031...  42.935951  \n",
-       "9  [0.00749532, 0.00577313, -0.016842585, -0.0015...  42.946739  "
+      "source": [
+        "!kaggle competitions download -c instacart-market-basket-analysis"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "K4Q4cOX-4_zY"
+      },
+      "source": [
+        "We must now extract the zip files."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "id": "f3g296nL4_zZ"
+      },
+      "outputs": [],
+      "source": [
+        "files = [\n",
+        "    'instacart-market-basket-analysis.zip',\n",
+        "    'order_products__train.csv.zip',\n",
+        "    'order_products__prior.csv.zip',\n",
+        "    'products.csv.zip',\n",
+        "    'orders.csv.zip'\n",
+        "]\n",
+        "\n",
+        "for filename in files:\n",
+        "    with zipfile.ZipFile(filename, 'r') as zip_ref:\n",
+        "        zip_ref.extractall('./')"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
     },
     {
-     "data": {
-      "text/html": [
-       "\n",
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-       "    <div>\n",
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-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>product_id</th>\n",
-       "      <th>product_name</th>\n",
-       "      <th>total_orders</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
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-       "      <th>0</th>\n",
-       "      <td>46149</td>\n",
-       "      <td>Zero Calorie Cola</td>\n",
-       "      <td>50</td>\n",
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-       "      fill: #174EA6;\n",
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-       "\n",
-       "    .colab-df-buttons div {\n",
-       "      margin-bottom: 4px;\n",
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-       "\n",
-       "    [theme=dark] .colab-df-convert {\n",
-       "      background-color: #3B4455;\n",
-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "      fill: #FFFFFF;\n",
-       "    }\n",
-       "  </style>\n",
-       "\n",
-       "    <script>\n",
-       "      const buttonEl =\n",
-       "        document.querySelector('#df-f8d32952-a2eb-4ff0-90ab-f61228ff9cbe button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-f8d32952-a2eb-4ff0-90ab-f61228ff9cbe');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
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-       "        docLink.innerHTML = docLinkHtml;\n",
-       "        element.appendChild(docLink);\n",
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-       "            style=\"display:none;\">\n",
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-       "     width=\"24px\">\n",
-       "    <g>\n",
-       "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
-       "    </g>\n",
-       "</svg>\n",
-       "  </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
-       "    background-color: #E8F0FE;\n",
-       "    border: none;\n",
-       "    border-radius: 50%;\n",
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-       "    fill: #1967D2;\n",
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-       "    padding: 0 0 0 0;\n",
-       "    width: 32px;\n",
-       "  }\n",
-       "\n",
-       "  .colab-df-quickchart:hover {\n",
-       "    background-color: #E2EBFA;\n",
-       "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
-       "    fill: #174EA6;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart {\n",
-       "    background-color: #3B4455;\n",
-       "    fill: #D2E3FC;\n",
-       "  }\n",
-       "\n",
-       "  [theme=dark] .colab-df-quickchart:hover {\n",
-       "    background-color: #434B5C;\n",
-       "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
-       "    fill: #FFFFFF;\n",
-       "  }\n",
-       "</style>\n",
-       "\n",
-       "  <script>\n",
-       "    async function quickchart(key) {\n",
-       "      const charts = await google.colab.kernel.invokeFunction(\n",
-       "          'suggestCharts', [key], {});\n",
-       "    }\n",
-       "    (() => {\n",
-       "      let quickchartButtonEl =\n",
-       "        document.querySelector('#df-3d17ff0c-1047-4f0c-888e-72d2296dc1c6 button');\n",
-       "      quickchartButtonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "oLgkRIfq4_zZ"
+      },
+      "source": [
+        "Now we can move on to loading the dataset. We'll first read the csv files and create dataframes."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "cBbbR7Rut5G_"
+      },
+      "outputs": [],
+      "source": [
+        "products = pd.read_csv('products.csv')\n",
+        "orders = pd.read_csv('orders.csv')\n",
+        "order_products = pd.concat([pd.read_csv('order_products__train.csv'), pd.read_csv('order_products__prior.csv')])"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5FV_GGjst5HA"
+      },
+      "source": [
+        "Since there isn't a user rating attribute, we'll gather \"confidence\" data by looking at the frequency of each item purchased by a user, and store this in the `data` dataframe."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YNgjd2nnqNx7"
+      },
+      "source": [
+        "### Data Manipulation"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "ZjRh7RYpt5HB"
+      },
+      "outputs": [],
+      "source": [
+        "customer_order_products = pd.merge(orders, order_products, how='inner',on='order_id')\n",
+        "\n",
+        "# create confidence table\n",
+        "data = customer_order_products.groupby(['user_id', 'product_id'])[['order_id']].count().reset_index()\n",
+        "data.columns=[\"user_id\", \"product_id\", \"total_orders\"]\n",
+        "data.product_id = data.product_id.astype('int64')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "77lvwm0St5HC"
+      },
+      "source": [
+        "Let's create a couple of test users to examine the recommendations later:\n",
+        "- 1st test user: buys 50 sodas: **Zero Calorie Cola**\n",
+        "- 2nd test user: buys organic produce: **Organic Whole Milk** and **Organic Blackberries**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 206
+        },
+        "id": "A06EfAf-t5HC",
+        "outputId": "95a1f51f-ced1-437a-8b62-569bb915262c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "          user_id  product_id  total_orders\n",
+              "13863744   206209       48697             1\n",
+              "13863745   206209       48742             2\n",
+              "13863746   206210       46149            50\n",
+              "13863747   206211       27845            49\n",
+              "13863748   206211       26604            32"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-b4f309ca-df44-4794-94f0-c1f5d4f92694\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>user_id</th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>total_orders</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>13863744</th>\n",
+              "      <td>206209</td>\n",
+              "      <td>48697</td>\n",
+              "      <td>1</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13863745</th>\n",
+              "      <td>206209</td>\n",
+              "      <td>48742</td>\n",
+              "      <td>2</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13863746</th>\n",
+              "      <td>206210</td>\n",
+              "      <td>46149</td>\n",
+              "      <td>50</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13863747</th>\n",
+              "      <td>206211</td>\n",
+              "      <td>27845</td>\n",
+              "      <td>49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>13863748</th>\n",
+              "      <td>206211</td>\n",
+              "      <td>26604</td>\n",
+              "      <td>32</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b4f309ca-df44-4794-94f0-c1f5d4f92694')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-b4f309ca-df44-4794-94f0-c1f5d4f92694 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-b4f309ca-df44-4794-94f0-c1f5d4f92694');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-8d271580-f613-47fb-93e0-bcf12569026d\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d271580-f613-47fb-93e0-bcf12569026d')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-8d271580-f613-47fb-93e0-bcf12569026d button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
       ],
-      "text/plain": [
-       "   product_id       product_name  total_orders\n",
-       "0       46149  Zero Calorie Cola            50"
+      "source": [
+        "data_new = pd.DataFrame([[data.user_id.max() + 1, 46149, 50],\n",
+        "                         [data.user_id.max() + 2, 27845, 49],\n",
+        "                         [data.user_id.max() + 2, 26604, 32]\n",
+        "                        ], columns=['user_id', 'product_id', 'total_orders'])\n",
+        "data = pd.concat([data, data_new]).reset_index(drop = True)\n",
+        "data.tail()"
       ]
-     },
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-     "output_type": "display_data"
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-       "      <td>[-0.011827968, 0.02923465, 0.006089752, -0.033...</td>\n",
-       "      <td>20.038776</td>\n",
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-       "      <th>8</th>\n",
-       "      <td>21137</td>\n",
-       "      <td>Organic Strawberries</td>\n",
-       "      <td>[-0.018719932, 0.004096488, -0.016034253, 0.02...</td>\n",
-       "      <td>20.056273</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>9</th>\n",
-       "      <td>47209</td>\n",
-       "      <td>Organic Hass Avocado</td>\n",
-       "      <td>[0.016230278, 0.0025620027, -0.0056362785, 0.0...</td>\n",
-       "      <td>20.078579</td>\n",
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-       "      fill: #D2E3FC;\n",
-       "    }\n",
-       "\n",
-       "    [theme=dark] .colab-df-convert:hover {\n",
-       "      background-color: #434B5C;\n",
-       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
-       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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-       "        document.querySelector('#df-5e743f8b-41b5-46ef-8a9f-ec9c452b0ac1 button.colab-df-convert');\n",
-       "      buttonEl.style.display =\n",
-       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
-       "\n",
-       "      async function convertToInteractive(key) {\n",
-       "        const element = document.querySelector('#df-5e743f8b-41b5-46ef-8a9f-ec9c452b0ac1');\n",
-       "        const dataTable =\n",
-       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
-       "                                                    [key], {});\n",
-       "        if (!dataTable) return;\n",
-       "\n",
-       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
-       "          + ' to learn more about interactive tables.';\n",
-       "        element.innerHTML = '';\n",
-       "        dataTable['output_type'] = 'display_data';\n",
-       "        await google.colab.output.renderOutput(dataTable, element);\n",
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-       "        element.appendChild(docLink);\n",
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-       "            title=\"Suggest charts.\"\n",
-       "            style=\"display:none;\">\n",
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-       "    </g>\n",
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-       "  </button>\n",
-       "\n",
-       "<style>\n",
-       "  .colab-df-quickchart {\n",
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-       "\n",
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-       "    background-color: #3B4455;\n",
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-       "      quickchartButtonEl.style.display =\n",
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-       "    })();\n",
-       "  </script>\n",
-       "</div>\n",
-       "    </div>\n",
-       "  </div>\n"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xBC-8PFTt5HD"
+      },
+      "source": [
+        "In the next step, we will extract user and product unique ids, in order to create a `CSR (Compressed Sparse Row)` matrix. This will allow us to perform collaborative filtering.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "v2_2R7zmt5HE"
+      },
+      "outputs": [],
+      "source": [
+        "# extract unique user and product ids\n",
+        "unique_users = list(np.sort(data.user_id.unique()))\n",
+        "unique_products = list(np.sort(products.product_id.unique()))\n",
+        "purchases = list(data.total_orders)\n",
+        "\n",
+        "# create zero-based index position <-> user/item ID mappings\n",
+        "index_to_user = pd.Series(unique_users)\n",
+        "\n",
+        "# create reverse mappings from user/item ID to index positions\n",
+        "user_to_index = pd.Series(data=index_to_user.index + 1, index=index_to_user.values)\n",
+        "\n",
+        "# create row and column for user and product ids\n",
+        "users_rows = data.user_id.astype(int)\n",
+        "products_cols = data.product_id.astype(int)\n",
+        "\n",
+        "# create CSR matrix\n",
+        "matrix = scipy.sparse.csr_matrix((purchases, (users_rows, products_cols)), shape=(len(unique_users) + 1, len(unique_products) + 1))\n",
+        "matrix.data = np.nan_to_num(matrix.data, copy=False)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "II6wOH96t5HF"
+      },
+      "source": [
+        "Let's now create a recommender model using the **implicit** library. The recommendation model is based off the algorithms described in the paper [Collaborative Filtering for Implicit Feedback Datasets](https://www.researchgate.net/publication/220765111_Collaborative_Filtering_for_Implicit_Feedback_Datasets) with performance optimizations described in [Applications of the Conjugate Gradient Method for Implicit Feedback Collaborative Filtering](https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.379.6473&rep=rep1&type=pdf).\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JDwIxGMnqNx8"
+      },
+      "source": [
+        "# Difference between colloborative and content filtering\n",
+        "\n",
+        "![picture](https://miro.medium.com/v2/resize:fit:1400/0*R8qw_CXxCc4600bQ.png)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 104,
+          "referenced_widgets": [
+            "51febb09c3d54a1a9cf5dd896f3a24f6",
+            "91b083fde4f14c39bbafb6fd099d44bd",
+            "84fca55b676b4ef2add284492c8f4c3c",
+            "bb2c985a09564562b6f040e31d817f07",
+            "cc06b425a9364b6eb07ef77c4ff6fc48",
+            "e2e92925bbb442f8a77e2d55886bfbfa",
+            "bc7f6859319f455da1f552b66a6cf026",
+            "66396eb857864cc8af94d7e2ced3102c",
+            "38ddb81c475a472d8439dcf72261b727",
+            "c095ad1b03a34c4e8b2077e373c82a5b",
+            "692c702c31904e058c809ae772f1579a"
+          ]
+        },
+        "id": "k0GW99kxt5HF",
+        "outputId": "548c2514-6194-43e4-dd24-6861f1808f5b"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/implicit/cpu/als.py:95: RuntimeWarning: OpenBLAS is configured to use 2 threads. It is highly recommended to disable its internal threadpool by setting the environment variable 'OPENBLAS_NUM_THREADS=1' or by calling 'threadpoolctl.threadpool_limits(1, \"blas\")'. Having OpenBLAS use a threadpool can lead to severe performance issues here.\n",
+            "  check_blas_config()\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/50 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "51febb09c3d54a1a9cf5dd896f3a24f6"
+            }
+          },
+          "metadata": {}
+        }
       ],
-      "text/plain": [
-       "   product_id          product_name  \\\n",
-       "0       26604  Organic Blackberries   \n",
-       "1       27966   Organic Raspberries   \n",
-       "2        9076           Blueberries   \n",
-       "3       43352           Raspberries   \n",
-       "4       39275   Organic Blueberries   \n",
-       "5       27845    Organic Whole Milk   \n",
-       "6       21288          Blackberries   \n",
-       "7       11777       Red Raspberries   \n",
-       "8       21137  Organic Strawberries   \n",
-       "9       47209  Organic Hass Avocado   \n",
-       "\n",
-       "                                              vector  _distance  \n",
-       "0  [-0.006724186, 0.025339324, 0.026328607, -0.00...  19.517328  \n",
-       "1  [-0.008532436, 0.012350272, 0.00061730895, 0.0...  19.688234  \n",
-       "2  [-0.02710966, 0.04093987, 0.051150266, -0.0465...  19.795025  \n",
-       "3  [-0.00842552, 0.01970873, 0.043075223, -0.0089...  19.805353  \n",
-       "4  [-0.01799259, 0.0049827565, 0.0029076852, 0.02...  19.961214  \n",
-       "5  [0.0005443055, -0.013880691, 0.008969757, -0.0...  19.976284  \n",
-       "6  [-0.007392233, -0.01224536, 0.03930769, 0.0020...  19.990463  \n",
-       "7  [-0.011827968, 0.02923465, 0.006089752, -0.033...  20.038776  \n",
-       "8  [-0.018719932, 0.004096488, -0.016034253, 0.02...  20.056273  \n",
-       "9  [0.016230278, 0.0025620027, -0.0056362785, 0.0...  20.078579  "
+      "source": [
+        "import os\n",
+        "os.environ[\"OPENBLAS_NUM_THREADS\"] = \"1\"\n",
+        "#split data into train and test splits\n",
+        "train, test = evaluation.train_test_split(matrix, train_percentage=0.9)\n",
+        "\n",
+        "# initialize the recommender model\n",
+        "model = implicit.als.AlternatingLeastSquares(factors=128,\n",
+        "                                             regularization=0.05,\n",
+        "                                             iterations=50,\n",
+        "                                             num_threads=1)\n",
+        "\n",
+        "alpha = 15\n",
+        "train = (train * alpha).astype('double')\n",
+        "\n",
+        "# train the model on CSR matrix\n",
+        "model.fit(train, show_progress = True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yN80hSojt5HF"
+      },
+      "source": [
+        "## Let's now evaluate the model."
       ]
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-       "\n",
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-       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 118,
+          "referenced_widgets": [
+            "ae94cc355e0c4f8b8b73824ae2ef5632",
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+          ]
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+        "id": "BbD8of_nt5HG",
+        "outputId": "0fd51c13-6aad-408c-8732-f634b900d88e"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "  0%|          | 0/192941 [00:00<?, ?it/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "ae94cc355e0c4f8b8b73824ae2ef5632"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'precision': 0.27412284342591836,\n",
+              " 'map': 0.04493413696144052,\n",
+              " 'ndcg': 0.14451615505158932,\n",
+              " 'auc': 0.6545342486842805}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 18
+        }
       ],
-      "text/plain": [
-       "   product_id          product_name  total_orders\n",
-       "0       27845    Organic Whole Milk            49\n",
-       "1       26604  Organic Blackberries            32"
+      "source": [
+        "test = (test * alpha).astype('double')\n",
+        "evaluation.ranking_metrics_at_k(model, train, test, K=100,\n",
+        "                         show_progress=True, num_threads=1)"
       ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Query by user factors\n",
-    "test_user_embeddings = test_user_factors.to_numpy().tolist() if device == \"cuda\" else test_user_factors.tolist()\n",
-    "for embedding, id in zip(test_user_embeddings, test_user_ids):\n",
-    "    results = tbl.search(embedding).limit(10).to_pandas()\n",
-    "    display(results)\n",
-    "    display(products_bought_by_user_in_the_past(id, top=15))"
-   ]
-  }
- ],
- "metadata": {
-  "accelerator": "GPU",
-  "colab": {
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+      "source": [
+        "From the model, we'll be able to retrieve item and user factors, which we can use later on to store in LanceDB as vector embeddings."
+      ]
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+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "38rssdYCBR4E"
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+      "source": [
+        "## Let's save the data and create a empty LanceDB Table using a Pydantic model.\n",
+        "A Table is designed to store large numbers of columns and huge quantities of data! For those interested, a LanceDB is columnar-based, and uses Lance, an open data format to store data."
+      ]
     },
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+    {
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+      "execution_count": 21,
+      "metadata": {
+        "id": "3_ykVLT6t5HH"
+      },
+      "outputs": [],
+      "source": [
+        "db = lancedb.connect(\"data/lancedb\")"
+      ]
     },
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+    {
+      "cell_type": "code",
+      "execution_count": 22,
+      "metadata": {
+        "id": "ufHsF0o4t5HI"
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+      "outputs": [],
+      "source": [
+        "class ProductModel(pydantic.BaseModel):\n",
+        "    product_id: int\n",
+        "    product_name: str\n",
+        "    vector: vector(128)\n",
+        "schema = pydantic_to_schema(ProductModel)\n",
+        "table_name = 'product_recommender'\n",
+        "tbl = db.create_table(table_name, schema=schema, mode=\"overwrite\")"
+      ]
     },
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+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0-2K-g4-t5HJ"
+      },
+      "source": [
+        "Let's now store our item factors into the table via the vector column of `product_entries`."
+      ]
     },
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+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "id": "NOOPF9zOt5HJ"
+      },
+      "outputs": [],
+      "source": [
+        "# Transform items into factors\n",
+        "items_factors = model.item_factors\n",
+        "product_entries = products[['product_id', 'product_name']].drop_duplicates()\n",
+        "product_entries['product_id'] = product_entries.product_id.astype('int64')\n",
+        "item_embeddings = items_factors[1:].tolist()\n",
+        "product_entries['vector'] = item_embeddings\n",
+        "\n",
+        "tbl.add(product_entries)"
+      ]
     },
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+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j3aU4z-tSbWE"
+      },
+      "source": [
+        "## Let's create an ANN index in order to speed up retrieval. This might take a while."
+      ]
     },
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-      "placeholder": "​",
-      "style": "IPY_MODEL_6ca9eb350a6b4a1b8bfdb500ce00c0c4",
-      "value": " 50/50 [00:25&lt;00:00,  1.93it/s]"
-     }
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "id": "H8HyvjCFSeaz"
+      },
+      "outputs": [],
+      "source": [
+        "tbl.create_index(num_partitions=256, num_sub_vectors=16)"
+      ]
     },
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+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ibNMrxyRt5HK"
+      },
+      "source": [
+        "This is a helper method for analysing recommendations later.\n",
+        "This method returns top N products that someone bought in the past (based on product quantity)."
+      ]
     },
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+      "cell_type": "code",
+      "execution_count": 27,
+      "metadata": {
+        "id": "Uzgk5Od0t5HK"
+      },
+      "outputs": [],
+      "source": [
+        "def products_bought_by_user_in_the_past(user_id: int, top: int = 10):\n",
+        "\n",
+        "    selected = data[data.user_id == user_id].sort_values(by=['total_orders'], ascending=False)\n",
+        "\n",
+        "    selected['product_name'] = selected['product_id'].map(product_entries.set_index('product_id')['product_name'])\n",
+        "    selected = selected[['product_id', 'product_name', 'total_orders']].reset_index(drop=True)\n",
+        "    if selected.shape[0] < top:\n",
+        "        return selected\n",
+        "\n",
+        "    return selected[:top]"
+      ]
     },
-    "aafb9c85a5b74f18bbbc41fc3132dd1e": {
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+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ULyVnHEXt5HK"
+      },
+      "source": [
+        "Let's retrieve our test users so we can query for recommendations."
+      ]
     },
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+    {
+      "cell_type": "code",
+      "execution_count": 28,
+      "metadata": {
+        "id": "Wwl7yFKTt5HK"
+      },
+      "outputs": [],
+      "source": [
+        "test_user_ids = [206210, 206211]\n",
+        "test_user_factors = model.user_factors[user_to_index[test_user_ids]]"
+      ]
     },
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-      "_view_module_version": "1.5.0",
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-      "layout": "IPY_MODEL_5e66de2e9882465b8ba1f252f049785a",
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-     }
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wTh61ou3t5HL"
+      },
+      "source": [
+        "## Let's now query LanceDB to retrieve recommendations."
+      ]
     },
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+      "execution_count": 31,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 866
+        },
+        "id": "UiZg4Iset5HL",
+        "outputId": "3f647136-a3ae-4a11-af57-f5c0ec37dd9e"
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+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   product_id                                       product_name  \\\n",
+              "0       46149                                  Zero Calorie Cola   \n",
+              "1         196                                               Soda   \n",
+              "2       40939                                     Drinking Water   \n",
+              "3       22802                                      Mineral Water   \n",
+              "4       37710                                          Trail Mix   \n",
+              "5       42500  Orange & Lemon Flavor Variety Pack Sparkling F...   \n",
+              "6       11759                    Organic Simply Naked Pita Chips   \n",
+              "7       41400                 Crunchy Oats 'n Honey Granola Bars   \n",
+              "8       46061                                            Popcorn   \n",
+              "9       26348                           Mixed Fruit Fruit Snacks   \n",
+              "\n",
+              "                                              vector  _distance  \n",
+              "0  [-0.014371638, -0.016776536, -0.026950998, -0....  36.209068  \n",
+              "1  [-0.031917833, -0.050772455, 0.013827451, -0.0...  36.464764  \n",
+              "2  [-0.013426425, 0.0053616967, -0.01992105, -0.0...  36.504112  \n",
+              "3  [-0.0062663523, -0.00076926383, -0.013624842, ...  36.615498  \n",
+              "4  [-0.01988333, -0.014069387, -0.021995109, -0.0...  36.650448  \n",
+              "5  [-0.009584657, -0.023491196, -0.033104196, -0....  36.696648  \n",
+              "6  [-0.009341286, -0.014609524, -0.0064758006, -0...  36.705814  \n",
+              "7  [-0.013461881, -0.021371827, -0.02064814, -0.0...  36.709579  \n",
+              "8  [0.0019679032, 0.00719048, -0.01262015, -0.005...  36.714954  \n",
+              "9  [-0.0017672281, 0.0020188452, 0.012172974, -0....  36.716858  "
+            ],
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+              "<style scoped>\n",
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+              "  <thead>\n",
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+              "      <th></th>\n",
+              "      <th>product_id</th>\n",
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+              "      <td>Zero Calorie Cola</td>\n",
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+              "      <td>36.209068</td>\n",
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+              "      <th>1</th>\n",
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+              "      <td>Soda</td>\n",
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+              "      <td>36.464764</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>40939</td>\n",
+              "      <td>Drinking Water</td>\n",
+              "      <td>[-0.013426425, 0.0053616967, -0.01992105, -0.0...</td>\n",
+              "      <td>36.504112</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>22802</td>\n",
+              "      <td>Mineral Water</td>\n",
+              "      <td>[-0.0062663523, -0.00076926383, -0.013624842, ...</td>\n",
+              "      <td>36.615498</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>37710</td>\n",
+              "      <td>Trail Mix</td>\n",
+              "      <td>[-0.01988333, -0.014069387, -0.021995109, -0.0...</td>\n",
+              "      <td>36.650448</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>42500</td>\n",
+              "      <td>Orange &amp; Lemon Flavor Variety Pack Sparkling F...</td>\n",
+              "      <td>[-0.009584657, -0.023491196, -0.033104196, -0....</td>\n",
+              "      <td>36.696648</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>11759</td>\n",
+              "      <td>Organic Simply Naked Pita Chips</td>\n",
+              "      <td>[-0.009341286, -0.014609524, -0.0064758006, -0...</td>\n",
+              "      <td>36.705814</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>41400</td>\n",
+              "      <td>Crunchy Oats 'n Honey Granola Bars</td>\n",
+              "      <td>[-0.013461881, -0.021371827, -0.02064814, -0.0...</td>\n",
+              "      <td>36.709579</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>46061</td>\n",
+              "      <td>Popcorn</td>\n",
+              "      <td>[0.0019679032, 0.00719048, -0.01262015, -0.005...</td>\n",
+              "      <td>36.714954</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>26348</td>\n",
+              "      <td>Mixed Fruit Fruit Snacks</td>\n",
+              "      <td>[-0.0017672281, 0.0020188452, 0.012172974, -0....</td>\n",
+              "      <td>36.716858</td>\n",
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+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-ac62f217-0463-46f5-8afe-9d2a2131c8a0 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-ac62f217-0463-46f5-8afe-9d2a2131c8a0');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-57871eeb-8636-47f6-ad5c-1ad2e0472592\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-57871eeb-8636-47f6-ad5c-1ad2e0472592')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-57871eeb-8636-47f6-ad5c-1ad2e0472592 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   product_id       product_name  total_orders\n",
+              "0       46149  Zero Calorie Cola            50"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-a8a797c1-376b-4c80-8aa9-4dae59a2047b\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>product_name</th>\n",
+              "      <th>total_orders</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>46149</td>\n",
+              "      <td>Zero Calorie Cola</td>\n",
+              "      <td>50</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a8a797c1-376b-4c80-8aa9-4dae59a2047b')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-a8a797c1-376b-4c80-8aa9-4dae59a2047b button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-a8a797c1-376b-4c80-8aa9-4dae59a2047b');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   product_id            product_name  \\\n",
+              "0       26604    Organic Blackberries   \n",
+              "1       43352             Raspberries   \n",
+              "2       27845      Organic Whole Milk   \n",
+              "3       21288            Blackberries   \n",
+              "4       27966     Organic Raspberries   \n",
+              "5        9076             Blueberries   \n",
+              "6       11777         Red Raspberries   \n",
+              "7       39275     Organic Blueberries   \n",
+              "8       21137    Organic Strawberries   \n",
+              "9       13176  Bag of Organic Bananas   \n",
+              "\n",
+              "                                              vector  _distance  \n",
+              "0  [0.045252558, 0.04258531, 0.011869884, -0.0111...  17.445852  \n",
+              "1  [0.059606433, 0.014409931, 0.008712215, -0.007...  17.617174  \n",
+              "2  [-0.03977351, 0.012210161, 0.024828656, 0.0155...  17.692816  \n",
+              "3  [0.030181486, 0.049021076, 0.003293778, -0.038...  17.696075  \n",
+              "4  [0.020116415, 0.045062356, 0.00675044, 0.01640...  17.872534  \n",
+              "5  [0.0482006, 0.06329333, -0.015093377, 0.000180...  17.879623  \n",
+              "6  [0.05492493, 0.008120705, 0.020613482, 0.00779...  17.931437  \n",
+              "7  [0.005109854, 0.032895964, -0.013481544, 0.010...  17.970798  \n",
+              "8  [0.0017651353, 0.033547334, -0.005775958, 0.02...  17.986570  \n",
+              "9  [0.004607136, 0.02749164, -0.006206838, 0.0187...  18.092993  "
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-c69b8cd2-9644-4ac9-94b4-645af39ba4e9\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>product_name</th>\n",
+              "      <th>vector</th>\n",
+              "      <th>_distance</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>26604</td>\n",
+              "      <td>Organic Blackberries</td>\n",
+              "      <td>[0.045252558, 0.04258531, 0.011869884, -0.0111...</td>\n",
+              "      <td>17.445852</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>43352</td>\n",
+              "      <td>Raspberries</td>\n",
+              "      <td>[0.059606433, 0.014409931, 0.008712215, -0.007...</td>\n",
+              "      <td>17.617174</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>27845</td>\n",
+              "      <td>Organic Whole Milk</td>\n",
+              "      <td>[-0.03977351, 0.012210161, 0.024828656, 0.0155...</td>\n",
+              "      <td>17.692816</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>21288</td>\n",
+              "      <td>Blackberries</td>\n",
+              "      <td>[0.030181486, 0.049021076, 0.003293778, -0.038...</td>\n",
+              "      <td>17.696075</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>27966</td>\n",
+              "      <td>Organic Raspberries</td>\n",
+              "      <td>[0.020116415, 0.045062356, 0.00675044, 0.01640...</td>\n",
+              "      <td>17.872534</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>5</th>\n",
+              "      <td>9076</td>\n",
+              "      <td>Blueberries</td>\n",
+              "      <td>[0.0482006, 0.06329333, -0.015093377, 0.000180...</td>\n",
+              "      <td>17.879623</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>6</th>\n",
+              "      <td>11777</td>\n",
+              "      <td>Red Raspberries</td>\n",
+              "      <td>[0.05492493, 0.008120705, 0.020613482, 0.00779...</td>\n",
+              "      <td>17.931437</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>7</th>\n",
+              "      <td>39275</td>\n",
+              "      <td>Organic Blueberries</td>\n",
+              "      <td>[0.005109854, 0.032895964, -0.013481544, 0.010...</td>\n",
+              "      <td>17.970798</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>8</th>\n",
+              "      <td>21137</td>\n",
+              "      <td>Organic Strawberries</td>\n",
+              "      <td>[0.0017651353, 0.033547334, -0.005775958, 0.02...</td>\n",
+              "      <td>17.986570</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>9</th>\n",
+              "      <td>13176</td>\n",
+              "      <td>Bag of Organic Bananas</td>\n",
+              "      <td>[0.004607136, 0.02749164, -0.006206838, 0.0187...</td>\n",
+              "      <td>18.092993</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c69b8cd2-9644-4ac9-94b4-645af39ba4e9')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-c69b8cd2-9644-4ac9-94b4-645af39ba4e9 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-c69b8cd2-9644-4ac9-94b4-645af39ba4e9');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-a4c5a95c-bd94-42c0-9696-d387ca47a1ae\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a4c5a95c-bd94-42c0-9696-d387ca47a1ae')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-a4c5a95c-bd94-42c0-9696-d387ca47a1ae button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "   product_id          product_name  total_orders\n",
+              "0       27845    Organic Whole Milk            49\n",
+              "1       26604  Organic Blackberries            32"
+            ],
+            "text/html": [
+              "\n",
+              "  <div id=\"df-8aeabba2-3cfa-49e6-a0d1-7ef6b343c6c6\" class=\"colab-df-container\">\n",
+              "    <div>\n",
+              "<style scoped>\n",
+              "    .dataframe tbody tr th:only-of-type {\n",
+              "        vertical-align: middle;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe tbody tr th {\n",
+              "        vertical-align: top;\n",
+              "    }\n",
+              "\n",
+              "    .dataframe thead th {\n",
+              "        text-align: right;\n",
+              "    }\n",
+              "</style>\n",
+              "<table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              "    <tr style=\"text-align: right;\">\n",
+              "      <th></th>\n",
+              "      <th>product_id</th>\n",
+              "      <th>product_name</th>\n",
+              "      <th>total_orders</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>0</th>\n",
+              "      <td>27845</td>\n",
+              "      <td>Organic Whole Milk</td>\n",
+              "      <td>49</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>26604</td>\n",
+              "      <td>Organic Blackberries</td>\n",
+              "      <td>32</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "    <div class=\"colab-df-buttons\">\n",
+              "\n",
+              "  <div class=\"colab-df-container\">\n",
+              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8aeabba2-3cfa-49e6-a0d1-7ef6b343c6c6')\"\n",
+              "            title=\"Convert this dataframe to an interactive table.\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
+              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
+              "  </svg>\n",
+              "    </button>\n",
+              "\n",
+              "  <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      padding: 0 0 0 0;\n",
+              "      width: 32px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert:hover {\n",
+              "      background-color: #E2EBFA;\n",
+              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "      fill: #174EA6;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-buttons div {\n",
+              "      margin-bottom: 4px;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert {\n",
+              "      background-color: #3B4455;\n",
+              "      fill: #D2E3FC;\n",
+              "    }\n",
+              "\n",
+              "    [theme=dark] .colab-df-convert:hover {\n",
+              "      background-color: #434B5C;\n",
+              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
+              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
+              "      fill: #FFFFFF;\n",
+              "    }\n",
+              "  </style>\n",
+              "\n",
+              "    <script>\n",
+              "      const buttonEl =\n",
+              "        document.querySelector('#df-8aeabba2-3cfa-49e6-a0d1-7ef6b343c6c6 button.colab-df-convert');\n",
+              "      buttonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "\n",
+              "      async function convertToInteractive(key) {\n",
+              "        const element = document.querySelector('#df-8aeabba2-3cfa-49e6-a0d1-7ef6b343c6c6');\n",
+              "        const dataTable =\n",
+              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
+              "                                                    [key], {});\n",
+              "        if (!dataTable) return;\n",
+              "\n",
+              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
+              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
+              "          + ' to learn more about interactive tables.';\n",
+              "        element.innerHTML = '';\n",
+              "        dataTable['output_type'] = 'display_data';\n",
+              "        await google.colab.output.renderOutput(dataTable, element);\n",
+              "        const docLink = document.createElement('div');\n",
+              "        docLink.innerHTML = docLinkHtml;\n",
+              "        element.appendChild(docLink);\n",
+              "      }\n",
+              "    </script>\n",
+              "  </div>\n",
+              "\n",
+              "\n",
+              "<div id=\"df-52e91118-2c73-48d6-9f11-d438046d11c2\">\n",
+              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-52e91118-2c73-48d6-9f11-d438046d11c2')\"\n",
+              "            title=\"Suggest charts\"\n",
+              "            style=\"display:none;\">\n",
+              "\n",
+              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
+              "     width=\"24px\">\n",
+              "    <g>\n",
+              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
+              "    </g>\n",
+              "</svg>\n",
+              "  </button>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "      --bg-color: #E8F0FE;\n",
+              "      --fill-color: #1967D2;\n",
+              "      --hover-bg-color: #E2EBFA;\n",
+              "      --hover-fill-color: #174EA6;\n",
+              "      --disabled-fill-color: #AAA;\n",
+              "      --disabled-bg-color: #DDD;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "      --bg-color: #3B4455;\n",
+              "      --fill-color: #D2E3FC;\n",
+              "      --hover-bg-color: #434B5C;\n",
+              "      --hover-fill-color: #FFFFFF;\n",
+              "      --disabled-bg-color: #3B4455;\n",
+              "      --disabled-fill-color: #666;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart {\n",
+              "    background-color: var(--bg-color);\n",
+              "    border: none;\n",
+              "    border-radius: 50%;\n",
+              "    cursor: pointer;\n",
+              "    display: none;\n",
+              "    fill: var(--fill-color);\n",
+              "    height: 32px;\n",
+              "    padding: 0;\n",
+              "    width: 32px;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart:hover {\n",
+              "    background-color: var(--hover-bg-color);\n",
+              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
+              "    fill: var(--button-hover-fill-color);\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-quickchart-complete:disabled,\n",
+              "  .colab-df-quickchart-complete:disabled:hover {\n",
+              "    background-color: var(--disabled-bg-color);\n",
+              "    fill: var(--disabled-fill-color);\n",
+              "    box-shadow: none;\n",
+              "  }\n",
+              "\n",
+              "  .colab-df-spinner {\n",
+              "    border: 2px solid var(--fill-color);\n",
+              "    border-color: transparent;\n",
+              "    border-bottom-color: var(--fill-color);\n",
+              "    animation:\n",
+              "      spin 1s steps(1) infinite;\n",
+              "  }\n",
+              "\n",
+              "  @keyframes spin {\n",
+              "    0% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "      border-left-color: var(--fill-color);\n",
+              "    }\n",
+              "    20% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    30% {\n",
+              "      border-color: transparent;\n",
+              "      border-left-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    40% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-top-color: var(--fill-color);\n",
+              "    }\n",
+              "    60% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "    }\n",
+              "    80% {\n",
+              "      border-color: transparent;\n",
+              "      border-right-color: var(--fill-color);\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "    90% {\n",
+              "      border-color: transparent;\n",
+              "      border-bottom-color: var(--fill-color);\n",
+              "    }\n",
+              "  }\n",
+              "</style>\n",
+              "\n",
+              "  <script>\n",
+              "    async function quickchart(key) {\n",
+              "      const quickchartButtonEl =\n",
+              "        document.querySelector('#' + key + ' button');\n",
+              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
+              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
+              "      try {\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      } catch (error) {\n",
+              "        console.error('Error during call to suggestCharts:', error);\n",
+              "      }\n",
+              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
+              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
+              "    }\n",
+              "    (() => {\n",
+              "      let quickchartButtonEl =\n",
+              "        document.querySelector('#df-52e91118-2c73-48d6-9f11-d438046d11c2 button');\n",
+              "      quickchartButtonEl.style.display =\n",
+              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "    })();\n",
+              "  </script>\n",
+              "</div>\n",
+              "    </div>\n",
+              "  </div>\n"
+            ]
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# Query by user factors\n",
+        "test_user_embeddings = test_user_factors.tolist()\n",
+        "for embedding, id in zip(test_user_embeddings, test_user_ids):\n",
+        "    results = tbl.search(embedding).limit(10).to_pandas()\n",
+        "    display(results)\n",
+        "    display(products_bought_by_user_in_the_past(id, top=15))"
+      ]
     },
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-      "_model_module": "@jupyter-widgets/controls",
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-      "_view_module_version": "1.5.0",
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-      "layout": "IPY_MODEL_95e65e6bceed40db9cea2a0adffed3fb",
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-      "value": " 192847/192847 [00:02&lt;00:00, 94872.39it/s]"
-     }
+    {
+      "cell_type": "code",
+      "source": [],
+      "metadata": {
+        "id": "-kWR644v1ZJp"
+      },
+      "execution_count": null,
+      "outputs": []
+    }
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