research-article

PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering

Authors:

Yuanxin Ouyang,

Zhang XiongAuthors Info & Claims

CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

Pages 1482 - 1492

https://doi.org/10.1145/3583780.3615009

Published: 21 October 2023 Publication History

Abstract

Collaborative filtering (CF) is the basic method for recommendation with implicit feedback. Recently, various state-of-the-art CF integrates graph neural networks. However, they often suffer from popularity bias, causing recommendations to deviate from users' genuine preferences. Additionally, several contrastive learning methods based on the in-batch sample strategy have been proposed to train the CF model effectively, but they are prone to suffering from sample bias. To address this problem, debiased contrastive loss has been employed in the recommendation, but instead of personalized debiasing, it treats each user equally. In this paper, we propose a popularity-aware debiased contrastive loss for CF, which can adaptively correct the positive and negative scores based on the popularity of users and items. Our approach aims to reduce the negative impact of popularity and sample bias simultaneously. We theoretically analyze the effectiveness of the proposed method and reveal the relationship between popularity and gradient, which justifies the correction strategy. We extensively evaluate our method on three public benchmarks over balanced and imbalanced settings. The results demonstrate its superiority over the existing debiased strategies, not only on the entire datasets but also when segmenting the datasets based on item popularity.

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Cited By

Zhang AYu YLi SGao RZhang LGao S(2024)Contrastive Learning-Based Personalized Tag RecommendationSensors10.3390/s2418606124:18(6061)Online publication date: 19-Sep-2024
https://doi.org/10.3390/s24186061
Liu BLuo QWang B(2024)Debiased Pairwise Learning for Implicit Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.347924036:12(7878-7892)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3479240
Chen GQiang WOuyang YYin CXiong Z(2024)Is Encoded Popularity Always Harmful? Explicit Debiasing with Augmentation for Contrastive Collaborative Filtering2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651019(1-9)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651019

Index Terms

PopDCL: Popularity-aware Debiased Contrastive Loss for Collaborative Filtering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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CIKM '23: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management

October 2023

5508 pages

ISBN:9798400701245

DOI:10.1145/3583780

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Published: 21 October 2023

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the State Key Laboratory of Software Development Environment of China
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October 21 - 25, 2023

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Cited By

Zhang AYu YLi SGao RZhang LGao S(2024)Contrastive Learning-Based Personalized Tag RecommendationSensors10.3390/s2418606124:18(6061)Online publication date: 19-Sep-2024
https://doi.org/10.3390/s24186061
Liu BLuo QWang B(2024)Debiased Pairwise Learning for Implicit Collaborative FilteringIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.347924036:12(7878-7892)Online publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1109/TKDE.2024.3479240
Chen GQiang WOuyang YYin CXiong Z(2024)Is Encoded Popularity Always Harmful? Explicit Debiasing with Augmentation for Contrastive Collaborative Filtering2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651019(1-9)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651019

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