GNN-based Recommendation Benchmark

Related tags

Deep Learning GRecX
Overview

GRecX

A Fair Benchmark for GNN-based Recommendation

Homepage and Documentation

Preliminary Comparison

LightGCN-Yelp dataset (featureless)

  • BCE-loss
Algo nDCG@5 nDCG@10 nDCG@15 nDCG@20
MF 0.031168 0.033510 0.037817 0.042061 (epoch:1300)
our-lightGCN 0.034872 0.037350 0.041520 0.045872 (epoch:1300)
  • BPR-loss
Algo nDCG@5 nDCG@10 nDCG@15 nDCG@20
MF 0.034672 0.037321 0.041864 0.046112
our-lightGCN 0.040223 0.042649 0.047568 0.052489 (epoch:1540)

LightGCN-Gowalla dataset (featureless)

  • BCE-loss
Algo nDCG@5 nDCG@10 nDCG@15 nDCG@20
MF --- --- --- 0.1298
our-lightGCN --- --- --- 0.1300
  • BPR-loss
Algo nDCG@5 nDCG@10 nDCG@15 nDCG@20
MF 0.116182 0.117339 0.123564 0.1400
our-lightGCN --- --- --- 0.1485

LightGCN-Amazon-book dataset (featureless)

Algo nDCG@20
lightGCN ---

Cite

If you use GRecX in a scientific publication, we would appreciate citations to the following paper:

@misc{cai2021grecx,
title={GRecX: An Efficient and Unified Benchmark for GNN-based Recommendation},
author={Desheng Cai and Jun Hu and Shengsheng Qian and Quan Fang and Quan Zhao and Changsheng Xu},
year={2021},
eprint={2111.10342},
archivePrefix={arXiv},
primaryClass={cs.IR}
}
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Comments
  • Hyperparameters tuning.

    Hyperparameters tuning.

    Hi, Thanks for making the code public! I have a single question about hyperparameters tuning.. In the original paper both NGCF and LightGCN, the authors mentioned that they split the data via train/valid/test. But there are no validation data in the author's Github code. Also, I could not find any validation data in your code.

    So, here is the question. How do you conduct hyperparameters tuning without any validation datasets? If you use a test dataset to tune hyperparameters, I think that is unfair. If you do not agree with this, please let me know. Best regards

    opened by MLATH 0
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