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

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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    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
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    Published: 07 July 2022

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    Author Tags

    1. contrastive learning
    2. data augmentation
    3. recommendation
    4. self-supervised learning

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    • Research-article

    Funding Sources

    • Australian Research Council Future Fellowship
    • Australian Research Council Discovery Early Career Research Award
    • Australian Research Council Discovery Project

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    • (2025)A Thorough Performance Benchmarking on Lightweight Embedding-based Recommender SystemsACM Transactions on Information Systems10.1145/3712589Online publication date: 17-Jan-2025
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