Content_based_RS_Final.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sarvesh1814/US-Book-Recommendation-System/blob/main/Content_based_RS_Final.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "metadata": {
        "id": "JwJZGejdYkAD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import scipy.stats\n",
        "from sklearn.metrics import precision_score\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "OpvM590GYkgC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Mount drive and load datasets"
      ],
      "metadata": {
        "id": "Cayp2XPRTzy4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Aj2DZqPPYXUV",
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      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ySgvYd_gXmDj"
      },
      "outputs": [],
      "source": [
        "Base_path ='/content/drive/MyDrive/Data/RS-3'"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Books with ISBN"
      ],
      "metadata": {
        "id": "DU4Hqc4VilBq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Book_ISBN = pd.read_csv(Base_path+\"/Books.csv\")"
      ],
      "metadata": {
        "id": "42P7WWp5ilCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Book_ISBN.rename(columns = {'Book-Title':'Book_Title'}, inplace = True)"
      ],
      "metadata": {
        "id": "CYXUDHip2kDu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Books with Description"
      ],
      "metadata": {
        "id": "i_Jl5E0sUN_T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Books = pd.read_csv(Base_path+\"/Books_Final.csv\")"
      ],
      "metadata": {
        "id": "o9WsIg0LadRB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Books.head()"
      ],
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          "height": 354
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        "id": "gC0Bnc06aewM",
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              "    categories                                        description  \n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "df=Books"
      ],
      "metadata": {
        "id": "gK7H1FQYSuGz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Rating Data"
      ],
      "metadata": {
        "id": "CqdWIDV6zoe1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Ratings = pd.read_csv(Base_path+\"/Ratings.csv\")"
      ],
      "metadata": {
        "id": "a11cPdGkznVO"
      },
      "execution_count": null,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "Ratings.head()"
      ],
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            "text/plain": [
              "   User-ID        ISBN  Book-Rating\n",
              "0   276725  034545104X            0\n",
              "1   276726  0155061224            5\n",
              "2   276727  0446520802            0\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### NLP preprocessing on Description Text"
      ],
      "metadata": {
        "id": "xekpys02zf_0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk\n",
        "nltk.download('omw-1.4')\n",
        "nltk.download(\"stopwords\")\n",
        "nltk.download(\"punkt\")\n",
        "nltk.download(\"wordnet\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGYdWMO-zWfm",
        "outputId": "0033b5e7-1fec-4cb4-d2a7-5428fad3e808"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Unzipping tokenizers/punkt.zip.\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import nltk\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer\n",
        "from nltk.tokenize import word_tokenize\n",
        "from nltk.stem import WordNetLemmatizer"
      ],
      "metadata": {
        "id": "u85S6z1szcf_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def clean_text(text):\n",
        "    \"\"\"\n",
        "    Clean the text by removing punctuation, stopwords, and converting to lowercase.\n",
        "    \"\"\"\n",
        "    # Remove punctuation\n",
        "    text = re.sub(r\"[^\\w\\s]\", \"\", text)\n",
        "    \n",
        "    # Convert to lowercase\n",
        "    text = text.lower()\n",
        "\n",
        "    # Remove non-English characters\n",
        "    text = re.sub(r\"[^\\x00-\\x7F]+\", \"\", text)\n",
        "    \n",
        "    # Tokenize the text\n",
        "    words = word_tokenize(text)\n",
        "    \n",
        "    # Remove stopwords\n",
        "    stop_words = set(stopwords.words(\"english\"))\n",
        "    words = [word for word in words if word not in stop_words]\n",
        "    \n",
        "    # Perform lemmatization\n",
        "    lemmatizer = WordNetLemmatizer()\n",
        "    words = [lemmatizer.lemmatize(word) for word in words]\n",
        "    \n",
        "    # Join the words back into a single string\n",
        "    cleaned_text = \" \".join(words)\n",
        "    \n",
        "    return cleaned_text"
      ],
      "metadata": {
        "id": "S2uxRSTozfa1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"category\"] = df[\"categories\"].apply(clean_text)"
      ],
      "metadata": {
        "id": "dtD4L1BKTbTx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"category\"].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CnXW1aQATfmI",
        "outputId": "6d2eb9be-400d-4e7c-bbaa-7fe4b11bd53a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    fiction\n",
              "1    fiction\n",
              "2    fiction\n",
              "3    fiction\n",
              "4    fiction\n",
              "Name: category, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
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        "outputId": "d5b8222b-b1cd-4ba6-8248-bb4bbc354bc9"
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      ]
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    {
      "cell_type": "code",
      "source": [
        "# Replace all non-letters with spaces # Search for all non-letters\n",
        "# define the function to remove non-alphabetic characters\n",
        "def remove_non_alphabetic(text):\n",
        "    return re.sub(\"[^a-zA-Z]\", \" \", str(text))\n",
        "\n",
        "# apply the function to each row of the \"description\" column\n",
        "df[\"description\"] = df[\"description\"].apply(remove_non_alphabetic)"
      ],
      "metadata": {
        "id": "Rr9iM6wOVtw7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"description\"] = df[\"description\"].apply(lambda x: clean_text(x))"
      ],
      "metadata": {
        "id": "bDe7w3XvZTPF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cleaning the Categories Column"
      ],
      "metadata": {
        "id": "0QpGhVjg7D8-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import string\n",
        "\n",
        "# Create a translation table for punctuation removal\n",
        "translator = str.maketrans(' ', ' ', string.punctuation)\n",
        "\n",
        "# Apply the translation table to the 'Review' column of the data frame\n",
        "df['category'] = df['category'].str.translate(translator)"
      ],
      "metadata": {
        "id": "g_sFZawz64AM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop('categories', axis='columns')"
      ],
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              "      <td>Heat</td>\n",
              "      <td>en</td>\n",
              "      <td>244.0</td>\n",
              "      <td>bestseller michael arroyo pitching arm throw s...</td>\n",
              "      <td>juvenile fiction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6574</th>\n",
              "      <td>6574</td>\n",
              "      <td>6574</td>\n",
              "      <td>Shadow Puppets (Ender, Book 7)</td>\n",
              "      <td>en</td>\n",
              "      <td>477.0</td>\n",
              "      <td>orson scott card award winning ender series ge...</td>\n",
              "      <td>bean fictitious character card</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6575 rows × 7 columns</p>\n",
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    {
      "cell_type": "markdown",
      "source": [
        "### Merging Categories and Description as major "
      ],
      "metadata": {
        "id": "6NRM6I5jzx4H"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Features'] = df['category'].astype(str) + ' ' + df['description'].astype(str)\n",
        "df.head(5)"
      ],
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        "colab": {
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        "outputId": "21fe92f3-2d61-47c9-88a2-e350a630f14a"
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              "                                                     [key], {});\n",
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              "\n",
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    {
      "cell_type": "markdown",
      "source": [
        "### Merge Rating & Book Feature DF"
      ],
      "metadata": {
        "id": "gPNpgWRk_Ed_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "book_merged = pd.merge(Books, Book_ISBN)"
      ],
      "metadata": {
        "id": "6XtirZd2x7CK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_rating = Ratings[Ratings['ISBN'].isin(book_merged['ISBN'])]"
      ],
      "metadata": {
        "id": "e8NvStw1DfrJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "book_merged.head()"
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          "height": 600
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        "id": "G_WQJJwP3yLb",
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        {
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              "      <td>http://images.amazon.com/images/P/0345323440.0...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Footfall</td>\n",
              "      <td>en</td>\n",
              "      <td>706</td>\n",
              "      <td>['Fiction']</td>\n",
              "      <td>hostile extraterrestrial known fithp invade ea...</td>\n",
              "      <td>fiction</td>\n",
              "      <td>fiction hostile extraterrestrial known fithp i...</td>\n",
              "      <td>0345323475</td>\n",
              "      <td>Larry Niven</td>\n",
              "      <td>1985</td>\n",
              "      <td>Ballantine Books</td>\n",
              "      <td>http://images.amazon.com/images/P/0345323475.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0345323475.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0345323475.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>The Reader</td>\n",
              "      <td>en</td>\n",
              "      <td>224</td>\n",
              "      <td>['Fiction']</td>\n",
              "      <td>hailed coiled eroticism moral claim make upon ...</td>\n",
              "      <td>fiction</td>\n",
              "      <td>fiction hailed coiled eroticism moral claim ma...</td>\n",
              "      <td>0679442790</td>\n",
              "      <td>Bernhard Schlink</td>\n",
              "      <td>1997</td>\n",
              "      <td>Random House</td>\n",
              "      <td>http://images.amazon.com/images/P/0679442790.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0679442790.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0679442790.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>The Reader</td>\n",
              "      <td>en</td>\n",
              "      <td>224</td>\n",
              "      <td>['Fiction']</td>\n",
              "      <td>hailed coiled eroticism moral claim make upon ...</td>\n",
              "      <td>fiction</td>\n",
              "      <td>fiction hailed coiled eroticism moral claim ma...</td>\n",
              "      <td>0375707972</td>\n",
              "      <td>Bernhard Schlink</td>\n",
              "      <td>1999</td>\n",
              "      <td>Vintage Books USA</td>\n",
              "      <td>http://images.amazon.com/images/P/0375707972.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0375707972.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0375707972.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>The Reader</td>\n",
              "      <td>en</td>\n",
              "      <td>224</td>\n",
              "      <td>['Fiction']</td>\n",
              "      <td>hailed coiled eroticism moral claim make upon ...</td>\n",
              "      <td>fiction</td>\n",
              "      <td>fiction hailed coiled eroticism moral claim ma...</td>\n",
              "      <td>0679781307</td>\n",
              "      <td>Carol Brown Janeway</td>\n",
              "      <td>1998</td>\n",
              "      <td>Random House Inc</td>\n",
              "      <td>http://images.amazon.com/images/P/0679781307.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0679781307.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0679781307.0...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e2264d72-32df-4ea4-8964-da43635f2574')\"\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 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\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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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e2264d72-32df-4ea4-8964-da43635f2574 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-e2264d72-32df-4ea4-8964-da43635f2574');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df=book_merged"
      ],
      "metadata": {
        "id": "B5NYQZLi4Ddf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Review Tokenization"
      ],
      "metadata": {
        "id": "KWpV9tHJ7-l6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Tokenize the data\n",
        "import nltk\n",
        "from nltk.tokenize import word_tokenize\n",
        "nltk.download('punkt')\n",
        "\n",
        "features = df['Features']\n",
        "tokenized_features = [word_tokenize(feature) for feature in features]"
      ],
      "metadata": {
        "id": "GcNGDdPK78xX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "83c8d6e4-69ca-476c-8280-b7803918ce3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Word Embeddings"
      ],
      "metadata": {
        "id": "u7ar_SBT8E-4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Word Embeddings Creation\n",
        "\n",
        "import gensim\n",
        "\n",
        "# Train the Word2Vec model\n",
        "model = gensim.models.Word2Vec(tokenized_features, vector_size=250, window=5, min_count=1, workers=8)\n",
        "# Access the word vectors\n",
        "word_vectors = model.wv"
      ],
      "metadata": {
        "id": "v7WLdwHS8CHH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "word_vectors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3pFMp6_DdkLU",
        "outputId": "60c4c176-2b3a-49c1-f209-9bb3fce862dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<gensim.models.keyedvectors.KeyedVectors at 0x7f9a37f216f0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Representing Description into Vectors"
      ],
      "metadata": {
        "id": "t8BsbKRV8NBn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['Features']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2kjI_W8Kl_j8",
        "outputId": "769805b3-cba6-4cd8-dd5c-78e5d80c2fb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       fiction hostile extraterrestrial known fithp i...\n",
              "1       fiction hailed coiled eroticism moral claim ma...\n",
              "2       fiction eleven year old nolitye granny used sa...\n",
              "3       fiction new york time bestseller troubled fami...\n",
              "4       fiction michael cunningham brings together pul...\n",
              "                              ...                        \n",
              "6570    philosophy inspirational teaching collection s...\n",
              "6571    fiction returning wyoming ranch civil war end ...\n",
              "6572    exile tale two city engrossing cross cultural ...\n",
              "6573    juvenile fiction bestseller michael arroyo pit...\n",
              "6574    bean fictitious character card orson scott car...\n",
              "Name: Features, Length: 6575, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "desc_array = []\n",
        "for feature in df['Features']:\n",
        "  word_vector = np.zeros((1, 250))\n",
        "  for word in word_tokenize(feature):\n",
        "    word_vector += word_vectors[word]\n",
        "  desc_array.append(word_vector) "
      ],
      "metadata": {
        "id": "LLENTBwy8MlP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "desc_array = np.array(desc_array)\n",
        "desc_array = desc_array.reshape(-1,250)\n",
        "desc_array.shape"
      ],
      "metadata": {
        "id": "lRSluKMj8KW_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2114c1ca-8fc6-4aeb-f4e6-56bf595c2270"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(6575, 250)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "desc_array"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sATjgmYbi-Ue",
        "outputId": "0d4e7436-ffb0-48dc-b6fe-6e2badc1112d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  2.58355247,  -0.11520911,   2.59334551, ...,  -1.24106617,\n",
              "         -1.58012329,   1.02832523],\n",
              "       [ 23.52885126,   1.04008594,  11.12106716, ...,  -8.84684407,\n",
              "        -10.43388766,   4.80989228],\n",
              "       [ 38.76853302,  -0.69275227,  18.52786057, ..., -12.82376173,\n",
              "        -17.01424097,   6.32691999],\n",
              "       ...,\n",
              "       [ 26.58901371,  -5.53397005,  21.24578739, ..., -10.97112045,\n",
              "        -14.56070025,  12.52903819],\n",
              "       [ 46.19840573,   1.9188477 ,  28.90117656, ..., -15.79473742,\n",
              "        -20.38117864,   9.85971584],\n",
              "       [ 18.73709538,  -2.87484076,  23.15604157, ...,  -7.32082503,\n",
              "        -10.53556924,  10.57495802]])"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cosine Similarity"
      ],
      "metadata": {
        "id": "CHIJs1mS8jLR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "similarities = cosine_similarity(desc_array,desc_array).flatten()"
      ],
      "metadata": {
        "id": "Gw8oBRFL8g8o"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "similarities"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PsIRjnOPf-o-",
        "outputId": "2f35eefd-5dfd-4494-87ec-14d6b06367d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1.        , 0.98017333, 0.97709128, ..., 0.98972698, 0.95777007,\n",
              "       1.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Count Vectorizer"
      ],
      "metadata": {
        "id": "MF44itc8mKgA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "count = CountVectorizer(stop_words='english')\n",
        "count_matrix = count.fit_transform(df['Features'])"
      ],
      "metadata": {
        "id": "Pi-v3pyADPd1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compute the Cosine Similarity matrix based on the count_matrix\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "cosine_sim2 = cosine_similarity(count_matrix, count_matrix)"
      ],
      "metadata": {
        "id": "frpbYznIDWko"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.reset_index()\n",
        "indices = pd.Series(df2.index, index=df2['ISBN'])"
      ],
      "metadata": {
        "id": "ffZreXsaDZWI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Personlized Recommendation for User ID"
      ],
      "metadata": {
        "id": "WOpMsiudMC4F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def recommend_books(ISBN, cosine_sim):\n",
        "    # Get the index of the books that matches the ISBN\n",
        "    idx = indices[ISBN]\n",
        "\n",
        "    # Get the pairwsie similarity scores of all books with that book\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "\n",
        "    # Sort the books based on the similarity scores\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "    \n",
        "\n",
        "    # Get the scores of the most similar books\n",
        "    sim_scores = sim_scores[1:21]\n",
        "\n",
        "    # Get the book indices\n",
        "    book_indices = [i[0] for i in sim_scores]\n",
        "\n",
        "    # Return the top most similar books\n",
        "    return df2[['Book_Title','ISBN']].iloc[book_indices], sim_scores"
      ],
      "metadata": {
        "id": "V_oTRIjgDaDI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def get_recommendations(userid, cosine_sim):\n",
        "    #userid = str(userid)\n",
        "    ISBN = df_rating['ISBN'].loc[df_rating[df_rating['User-ID'] == userid]['Book-Rating'].idxmax()]\n",
        "\n",
        "    recommendation, sim_score = recommend_books(ISBN, cosine_sim)\n",
        "\n",
        "    df_recommend = pd.DataFrame(recommendation)\n",
        "    df_recommend['Cosine_Similarity'] = sim_score\n",
        "    df_recommend['Cosine_Similarity'] = df_recommend['Cosine_Similarity'].apply(lambda x: x[1])\n",
        "\n",
        "    return df_recommend"
      ],
      "metadata": {
        "id": "Ym0fuDXD4M69"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_recommend = get_recommendations(8, cosine_sim2)\n",
        "df_recommend=df_recommend.drop_duplicates(subset='Book_Title')\n",
        "df_recommend[['Book_Title','ISBN']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "IN7wvXQy47Tr",
        "outputId": "3f798257-fba7-4e77-d2e7-6d9e10dd8fff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              Book_Title        ISBN\n",
              "12698    Flu : The Story Of The Great Influenza Pandemic  0743203984\n",
              "5704                                            Outbreak  042510687X\n",
              "8667                                          Fatherland  0061006629\n",
              "1910         The Illustrated Man (Grand Master Editions)  055327449X\n",
              "10301  Farewell to Manzanar: A True Story of Japanese...  0553272586"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4f01e531-b27f-4e0b-af18-183d5fb0faf7\">\n",
              "    <div 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",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Book_Title</th>\n",
              "      <th>ISBN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12698</th>\n",
              "      <td>Flu : The Story Of The Great Influenza Pandemic</td>\n",
              "      <td>0743203984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5704</th>\n",
              "      <td>Outbreak</td>\n",
              "      <td>042510687X</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8667</th>\n",
              "      <td>Fatherland</td>\n",
              "      <td>0061006629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1910</th>\n",
              "      <td>The Illustrated Man (Grand Master Editions)</td>\n",
              "      <td>055327449X</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10301</th>\n",
              "      <td>Farewell to Manzanar: A True Story of Japanese...</td>\n",
              "      <td>0553272586</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4f01e531-b27f-4e0b-af18-183d5fb0faf7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "      \n",
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              "\n",
              "    [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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              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-4f01e531-b27f-4e0b-af18-183d5fb0faf7');\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",
              "          element.innerHTML = '';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Recommendation Based on ISBN"
      ],
      "metadata": {
        "id": "PiLxfeJYMThn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a function to get the top K similar books for each book\n",
        "def get_similar_books(ISBN, cosine_sim, k):\n",
        "\n",
        "    # Get the row index of the book in the cosine similarity matrix\n",
        "    idx = book_merged[book_merged['ISBN'] == ISBN].index[0]\n",
        "\n",
        "    # Get the pairwise cosine similarity scores for the book\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "\n",
        "    # Sort the books based on the similarity scores\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Get the book indices of the top K similar books\n",
        "    book_indices = [i[0] for i in sim_scores[1:k+1]]\n",
        "\n",
        "    # Return the top K similar books\n",
        "    return df2[['Book_Title','ISBN']].iloc[book_indices]"
      ],
      "metadata": {
        "id": "oXYY6fAADx0O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_recommend = get_similar_books(\"0743203984\", cosine_sim2,20)\n",
        "df_recommend=df_recommend.drop_duplicates(subset='Book_Title')\n",
        "df_recommend"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "X5Y88_zxFNJD",
        "outputId": "96f3bc0c-805c-4c92-da34-1323ff68f3a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              Book_Title        ISBN\n",
              "12698    Flu : The Story Of The Great Influenza Pandemic  0743203984\n",
              "5704                                            Outbreak  042510687X\n",
              "8667                                          Fatherland  0061006629\n",
              "1910         The Illustrated Man (Grand Master Editions)  055327449X\n",
              "10301  Farewell to Manzanar: A True Story of Japanese...  0553272586\n",
              "7820                                     Spandau Phoenix  0451179803\n",
              "6632                          The Years of Rice and Salt  0553580078\n",
              "9406                                 My American Journey  0345407288\n",
              "7154                            The Demon in the Freezer  0345466632\n",
              "7736             The Demon in the Freezer : A True Story  0375508562\n",
              "9291   The Coming Plague: Newly Emerging Diseases in ...  0374126461"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dbd3c71b-ffb5-496a-acfe-97b68d2c8b01\">\n",
              "    <div 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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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Book_Title</th>\n",
              "      <th>ISBN</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12698</th>\n",
              "      <td>Flu : The Story Of The Great Influenza Pandemic</td>\n",
              "      <td>0743203984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5704</th>\n",
              "      <td>Outbreak</td>\n",
              "      <td>042510687X</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8667</th>\n",
              "      <td>Fatherland</td>\n",
              "      <td>0061006629</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1910</th>\n",
              "      <td>The Illustrated Man (Grand Master Editions)</td>\n",
              "      <td>055327449X</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10301</th>\n",
              "      <td>Farewell to Manzanar: A True Story of Japanese...</td>\n",
              "      <td>0553272586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7820</th>\n",
              "      <td>Spandau Phoenix</td>\n",
              "      <td>0451179803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6632</th>\n",
              "      <td>The Years of Rice and Salt</td>\n",
              "      <td>0553580078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9406</th>\n",
              "      <td>My American Journey</td>\n",
              "      <td>0345407288</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7154</th>\n",
              "      <td>The Demon in the Freezer</td>\n",
              "      <td>0345466632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7736</th>\n",
              "      <td>The Demon in the Freezer : A True Story</td>\n",
              "      <td>0375508562</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9291</th>\n",
              "      <td>The Coming Plague: Newly Emerging Diseases in ...</td>\n",
              "      <td>0374126461</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-dbd3c71b-ffb5-496a-acfe-97b68d2c8b01');\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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Coverage Evaluation"
      ],
      "metadata": {
        "id": "KDEBFsGb-TSq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### k=10"
      ],
      "metadata": {
        "id": "91QDpLEGW67F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Define recommendation function\n",
        "def get_recommendations(ISBN, cosine_similarities=cosine_sim2, indices=indices):\n",
        "    # Get index of book\n",
        "    idx = indices[ISBN]\n",
        "\n",
        "    # Get pairwise similarity scores\n",
        "    sim_scores = list(enumerate(cosine_sim2[idx]))\n",
        "\n",
        "    # Sort by similarity scores\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Get top 10 most similar books\n",
        "    sim_scores = sim_scores[1:11]\n",
        "\n",
        "    # Get book indices\n",
        "    book_indices = [i[0] for i in sim_scores]\n",
        "\n",
        "    # Return recommended book ISBNs\n",
        "    return df[['Book_Title','ISBN']].iloc[book_indices]"
      ],
      "metadata": {
        "id": "5r16owWc5ucK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get all unique book titles in the dataset\n",
        "all_books = set(df['ISBN'])\n",
        "\n",
        "# Define function to get recommended books for all titles\n",
        "def get_all_recommendations(cosine_similarities=cosine_sim2, indices=indices):\n",
        "    all_recommendations = []\n",
        "    for title in all_books:\n",
        "        recommendations = get_recommendations(title, cosine_sim2, indices)\n",
        "        all_recommendations.extend(list(recommendations))\n",
        "    return all_recommendations\n",
        "\n",
        "# Get all recommended books\n",
        "all_recommendations = set(get_all_recommendations())\n",
        "\n",
        "# Calculate coverage\n",
        "coverage = len(all_recommendations) / len(all_books) * 100\n",
        "\n",
        "print(f\"Coverage of content-based system: {coverage:.2f}%\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y9KYqwUhUqT7",
        "outputId": "07fc57cf-1332-490d-eecb-0d0c239323d2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Coverage of content-based system with only Book data: 88.55%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tYEK0y1OWhhp"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}