GNN-based Recommendation Benchmark

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Deep LearningGRecX
Overview

GRecX

A Fair Benchmark for GNN-based Recommendation

Homepage and Documentation

Preliminary Comparison

LightGCN-Yelp dataset (featureless)

  • BCE-loss
Algo [email protected] [email protected] [email protected] [email protected]
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 [email protected] [email protected] [email protected] [email protected]
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 [email protected] [email protected] [email protected] [email protected]
MF --- --- --- 0.1298
our-lightGCN --- --- --- 0.1300
  • BPR-loss
Algo [email protected] [email protected] [email protected] [email protected]
MF 0.116182 0.117339 0.123564 0.1400
our-lightGCN --- --- --- 0.1485

LightGCN-Amazon-book dataset (featureless)

Algo [email protected]
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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