DecGCN is the code for our paper "Decoupled Graph Convolution Network for Inferring Substitutable and Complementary Items", which is published in CIKM 2020.
The code is also available at https://github.com/liuyiding1993/CIKM2020_DecGCN.
Please cite the following paper if you use the code in any way.
- Step1: Download meta data from https://nijianmo.github.io/amazon/index.html.
- Step2: Put the meta data file in ./preprocessing/raw_data/.
- Step3: Set the dataset name (i.e., $dataset) in run.sh, run preprocessing by cd preprocessing; sh run.sh.
The compressed data files (i.e., .dat files) will be put in ./euler_data/$dataset_name/.
python run_loop.py --mode=train --data_dir=./euler_data/Beauty \
--max_id=114791 --sparse_feature_max_id=10,44,11178 \
--dim=128 --embedding_dim=16 --num_negs=5 --fanouts=5,5 \
--model=DecGCN --model_dir=ckpt --batch_size=512 \
--optimizer=adam --learning_rate=1e-4 --num_epochs=20 --log_steps=20| Name | Type | Description |
|---|---|---|
| mode | enum(str) | train, evaluate or save_embedding. |
| data_dir | str | directory of the specified dataset (e.g., ./euler_data/Beauty). |
| max_id | int | maximum node id, i.e., the number of nodes - 1. |
| sparse_feature_max_id | list(int) | list of maximum feature id. |
| dim | int | dimensionality of hidden layers. |
| embedding_dim | int | dimensionality of feature embeddings. |
| num_negs | int | number of negative samples during training. |
| fanouts | list(int) | numbers of sampled neighbors. |
| model | str | model to be trained (e.g., DecGCN). |
| model_dir | str | directory to save/load a model. |
| batch_size | int | training batch size. |
| optimizer | enum(str) | training optimizer (e.g., adam or sgd). |
| learning_rate | float | learning rate for training. |
| num_epochs | int | number of passes over the training data. |
| log_steps | int | number of batches to print the log info. |