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Soft Contrastive Learning for Implicit Feedback Recommendations

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Advances in Knowledge Discovery and Data Mining (PAKDD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14649))

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Abstract

Collaborative filtering (CF) plays a crucial role in the development of recommendations. Most CF research focuses on implicit feedback due to its accessibility, but deriving user preferences from such feedback is challenging given the inherent noise in interactions. Existing works primarily employ unobserved interactions as negative samples, leading to a critical noisy-label problem. In this study, we propose SCLRec (Soft Contrastive Learning for Recommendations), a novel method to alleviate the noise issue in implicit recommendations. To this end, we first construct a similarity matrix based on user and item embeddings along with item popularity information. Subsequently, to leverage information from nearby samples, we employ entropy optimal transport to obtain the matching matrix from the similarity matrix. The matching matrix provides additional supervisory signals that uncover matching relationships of unobserved user-item interactions, thereby mitigating the noise issue. Finally, we treat the matching matrix as soft targets, and use them to train the model via contrastive learning loss. Thus, we term it soft contrastive learning, which combines the denoising capability of soft targets with the representational strength of contrastive learning to enhance implicit recommendations. Extensive experiments on three public datasets demonstrate that SCLRec achieves consistent performance improvements compared to state-of-the-art CF methods.

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Acknowledgements

This work was partially supported by NSFC (62122037) and the Collaborative Innovation Center of Novel Software Technology and Industrialization. We thank Zi-Hao Qiu for the helpful discussions.

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Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China

    Zhen-Hua Zhuang & Lijun Zhang

  2. School of Artificial Intelligence, Nanjing University, Nanjing, China

    Zhen-Hua Zhuang & Lijun Zhang

Authors
  1. Zhen-Hua Zhuang
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  2. Lijun Zhang
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Corresponding author

Correspondence to Lijun Zhang .

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Editors and Affiliations

  1. Taipei, Taiwan

    De-Nian Yang

  2. Microsoft Research Asia, Beijing, China

    Xing Xie

  3. National Yang Ming Chiao Tung University, Hsinchu, Taiwan

    Vincent S. Tseng

  4. Duke University, Durham, NC, USA

    Jian Pei

  5. National Cheng Kung University, Tainan, Taiwan

    Jen-Wei Huang

  6. Silesian University of Technology, Gliwice, Poland

    Jerry Chun-Wei Lin

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Zhuang, ZH., Zhang, L. (2024). Soft Contrastive Learning for Implicit Feedback Recommendations. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14649. Springer, Singapore. https://doi.org/10.1007/978-981-97-2262-4_18

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  • DOI: https://doi.org/10.1007/978-981-97-2262-4_18

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2264-8

  • Online ISBN: 978-981-97-2262-4

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