Handling Information Loss of Graph Neural Networks for Session-based Recommendation

Overview

LESSR

A PyTorch implementation of LESSR (Lossless Edge-order preserving aggregation and Shortcut graph attention for Session-based Recommendation) from the paper:
Handling Information Loss of Graph Neural Networks for Session-based Recommendation, Tianwen Chen and Raymong Chi-Wing Wong, KDD '20

Requirements

  • PyTorch 1.6.0
  • NumPy 1.19.1
  • Pandas 1.1.3
  • DGL 0.5.2

Usage

  1. Install the requirements.
    If you use Anaconda, you can create a conda environment with the required packages using the following command.

    conda env create -f packages.yml

    Activate the created conda environment.

    conda activate lessr
    
  2. Download and extract the datasets.

  3. Preprocess the datasets using preprocess.py.
    For example, to preprocess the Diginetica dataset, extract the file train-item-views.csv to the folder datasets/ and run the following command:

    python preprocess.py -d diginetica -f datasets/train-item-views.csv

    The preprocessed dataset is stored in the folder datasets/diginetica.
    You can see the detailed usage of preprocess.py by running the following command:

    python preprocess.py -h
  4. Train the model using main.py.
    If no arguments are passed to main.py, it will train a model using a sample dataset with default hyperparameters.

    python main.py

    The commands to train LESSR with suggested hyperparameters on different datasets are as follows:

    python main.py --dataset-dir datasets/diginetica --embedding-dim 32 --num-layers 4
    python main.py --dataset-dir datasets/gowalla --embedding-dim 64 --num-layers 4
    python main.py --dataset-dir datasets/lastfm --embedding-dim 128 --num-layers 4

    You can see the detailed usage of main.py by running the following command:

    python main.py -h
  5. Use your own dataset.

    1. Create a subfolder in the datasets/ folder.
    2. The subfolder should contain the following 3 files.
      • num_items.txt: This file contains a single integer which is the number of items in the dataset.
      • train.txt: This file contains all the training sessions.
      • test.txt: This file contains all the test sessions.
    3. Each line of train.txt and test.txt represents a session, which is a list of item IDs separated by commas. Note the item IDs must be in the range of [0, num_items).
    4. See the folder datasets/sample for an example of a dataset.

Citation

If you use our code in your research, please cite our paper:

@inproceedings{chen2020lessr,
    title="Handling Information Loss of Graph Neural Networks for Session-based Recommendation",
    author="Tianwen {Chen} and Raymond Chi-Wing {Wong}",
    booktitle="Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD '20)",
    pages="1172-–1180",
    year="2020"
}
Owner
Tianwen CHEN
A CS PhD Student in HKUST
Tianwen CHEN
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