research-article

Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation

Authors:

Quoc Viet Hung NguyenAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1294 - 1303

https://doi.org/10.1145/3477495.3531937

Published: 07 July 2022 Publication History

Abstract

Contrastive learning (CL) recently has spurred a fruitful line of research in the field of recommendation, since its ability to extract self-supervised signals from the raw data is well-aligned with recommender systems' needs for tackling the data sparsity issue. A typical pipeline of CL-based recommendation models is first augmenting the user-item bipartite graph with structure perturbations, and then maximizing the node representation consistency between different graph augmentations. Although this paradigm turns out to be effective, what underlies the performance gains is still a mystery. In this paper, we first experimentally disclose that, in CL-based recommendation models, CL operates by learning more uniform user/item representations that can implicitly mitigate the popularity bias. Meanwhile, we reveal that the graph augmentations, which used to be considered necessary, just play a trivial role. Based on this finding, we propose a simple CL method which discards the graph augmentations and instead adds uniform noises to the embedding space for creating contrastive views. A comprehensive experimental study on three benchmark datasets demonstrates that, though it appears strikingly simple, the proposed method can smoothly adjust the uniformity of learned representations and has distinct advantages over its graph augmentation-based counterparts in terms of recommendation accuracy and training efficiency. The code is released at https://github.com/Coder-Yu/QRec.

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Index Terms

Are Graph Augmentations Necessary?: Simple Graph Contrastive Learning for Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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Australian Research Council Future Fellowship
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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Cited By

Yong JWei JLei XDang JLu WCheng M(2025)A Learning Resource Recommendation Method Based on Graph Contrastive LearningElectronics10.3390/electronics1401014214:1(142)Online publication date: 1-Jan-2025
https://doi.org/10.3390/electronics14010142
Wei TYang CZheng Y(2025)Prototypical Graph Contrastive Learning for RecommendationApplied Sciences10.3390/app1504196115:4(1961)Online publication date: 13-Feb-2025
https://doi.org/10.3390/app15041961
Tran HChen TQuoc Viet Hung NHuang ZCui LYin H(2025)A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsACM Transactions on Information Systems10.1145/3712589Online publication date: 17-Jan-2025
https://doi.org/10.1145/3712589
Yu LHu JDu QNiu X(2025)MVideoRec: Micro Video Recommendations through Modality Decomposition and Contrastive LearningACM Transactions on Information Systems10.1145/371185543:3(1-27)Online publication date: 24-Jan-2025
https://dl.acm.org/doi/10.1145/3711855
Peng YGao CZhang YDan TDu XLuo HLi YMeng X(2025)Denoising Alignment with Large Language Model for RecommendationACM Transactions on Information Systems10.1145/369666243:2(1-35)Online publication date: 24-Jan-2025
https://dl.acm.org/doi/10.1145/3696662
Niu JZhou WLuo FZhang YZeng JWen J(2025)Intent-guided Bilateral Long and Short-Term Information Mining with Contrastive Learning for Sequential RecommendationIEEE Transactions on Services Computing10.1109/TSC.2024.3520868(1-15)Online publication date: 2025
https://doi.org/10.1109/TSC.2024.3520868
Zhang THou CJiang RZhang XZhou CTang KLv H(2025)Label Informed Contrastive Pretraining for Node Importance Estimation on Knowledge GraphsIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.336369536:3(4462-4476)Online publication date: Mar-2025
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Qin ZCheng WDing WDing G(2025)Hyperbolic Graph Contrastive Learning for Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.352296037:3(1255-1267)Online publication date: Mar-2025
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Wang ZFeng YZhang XYang RDu B(2025)Multi-Modal Correction Network for RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.349337437:2(810-822)Online publication date: Feb-2025
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Chen HBei YHuang WChen SHuang FHuang X(2025)Graph Cross-Correlated Network for RecommendationIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.349177837:2(710-723)Online publication date: Feb-2025
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