[SIGIR22] Official PyTorch implementation for "CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space".

Related tags

Deep Learning CORE
Overview

CORE

This is the official PyTorch implementation for the paper:

Yupeng Hou, Binbin Hu, Zhiqiang Zhang, Wayne Xin Zhao. CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space. SIGIR 2022 short.

Overview

We argue that session embedding encoded by non-linear encoder is usually not in the same representation space as item embeddings, resulting in the inconsistent prediction issue. In this work, we aim at unifying the representation space throughout the encoding and decoding process in session-based recommendation, and propose a simple and effective framework named CORE.

Requirements

recbole>=1.0.0
python==3.7
pytorch==1.7.1
cudatoolkit==10.1

Datasets

you can download the processed datasets from Google Drive. Then,

mv DATASET.zip dataset
unzip DATASET.zip

DATASET can be one of

  • diginetica
  • nowplaying
  • retailrocket
  • tmall
  • yoochoose

Reproduction

python main.py --model MODEL --dataset DATASET

MODEL can be one of

  • trm
  • ave

Results

Here we show results on Diginetica for example, other results can be found in our paper.

Note that we use a more standard and unified pipeline for fair evaluation and preventing overfitting.

We split the sessions in each dataset into train/validation/test set in temporal order in a ratio of 8:1:1. We report results on the test set with models that gain the highest performance on the validation set.

Model R@20 MRR@20
CORE-ave 50.21 18.07
CORE-trm 52.89 18.58

Acknowledgement

The implementation is based on the open-source recommendation library RecBole and RecBole-GNN.

Please cite the following papers as the references if you use our codes or the processed datasets.

@inproceedings{hou2022core,
  author = {Yupeng Hou and Binbin Hu and Zhiqiang Zhang and Wayne Xin Zhao},
  title = {CORE: Simple and Effective Session-based Recommendation within Consistent Representation Space},
  booktitle = {{SIGIR}},
  year = {2022}
}


@inproceedings{zhao2021recbole,
  title={Recbole: Towards a unified, comprehensive and efficient framework for recommendation algorithms},
  author={Wayne Xin Zhao and Shanlei Mu and Yupeng Hou and Zihan Lin and Kaiyuan Li and Yushuo Chen and Yujie Lu and Hui Wang and Changxin Tian and Xingyu Pan and Yingqian Min and Zhichao Feng and Xinyan Fan and Xu Chen and Pengfei Wang and Wendi Ji and Yaliang Li and Xiaoling Wang and Ji-Rong Wen},
  booktitle={{CIKM}},
  year={2021}
}
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Comments
  • raw datasets

    raw datasets

    Hi,

    Thanks for open-sourcing your research! Especially for the datasets and requirements ;) Could you please also provide a link for raw datasets download?

    opened by Scitator 5
  • can't run successfully

    can't run successfully

    Hi, I really appreciate your work "CORE". After reading the paper, I couldn't wait to download and run your open source code. However, there is an error here "ValueError: train_neg_sample_args [{'distribution': 'uniform', 'sample_num': 1, 'alpha': 1.0, 'dynamic': False, 'candidate_num': 0}] should be None when the loss_type is CE.". I can't find the reason. So I'd really appreciate it if you could help me.

    opened by Doctor-JZL 2
  • Session dataset splitting

    Session dataset splitting

    你好，excellent work! 关于实验部分我想请教一下，论文当中提到将 session 数据集按 8:1:1 的比例划分 train/val/test，代码中的 config 为
    split: RS: [0.8, 0.1, 0.1] 我之前读 recbole 源码的时候，这种方式应该是将一个 session 序列按 8:1:1 分开，也就说如果一个会话长度为10，则前8个item为训练集，第9个item为验证，第10个item 为测试集，请问我这样理解是正确的吗？ 因为现在会话推荐领域大部分工作的划分是基于 session 为最小单位，即如果 1000 个session，那么前900个训练，100验证100测试。 我在自己实现 CORE-ave 的时候无法复现结果，突然想到这个问题，请不吝赐教，感谢！🙏

    opened by WangZesheng12 2
  • Request for reproduction of Star-GNN

    Request for reproduction of Star-GNN

    Hi, author. Your work "CORE" is concise and novelty. Thanks to your open source code, I have succeeded in reproducing your work and got a successful result. But there exists one thing I want to ask you about the reproduction of baseline Star-GNN which had been reproduced by you as sad in your paper. I would appreciate it if you could send me the code of Star-GNN since I want to test this model with your idea. Best wishes

    opened by Ethanhack 1
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