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FairCF: fairness-aware collaborative filtering

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Abstract

Collaborative filtering (CF) techniques learn user and item embeddings from user-item interaction behaviors, and are commonly used in recommendation systems to help users find potentially desirable items. Most CF models optimize recommendation accuracy; however, they may lead to unwanted biases for particular demographic groups. Thus, we focus on learning fair representations of CF-based recommendations. We formulate this problem as an optimization task with two competing goals: embedding representations better meet accuracy requirements of recommendations, and simultaneously obfuscate information hidden in the embedding space, which is related to the users’ sensitive attributes for fairness. Here, the intuitive idea is to use fair representation learning from machine learning to train a classifier with a sensitive attribute predictor from the user side to satisfy the fairness goal. However, such fair machine learning models assume entity independence, which differs greatly from CF because users and items are correlated collaboratively via user-item behaviors. Therefore, sensitive user information can be exposed from the users’ preferred items. Consequently, defining only fairness constraints on users cannot achieve fairness in recommendation systems. In this paper, we propose FairCF framework for fairness-aware collaborative filtering. In particular, we first define fairness constraints in a fair embedding space, where both a user classifier and an item classifier are employed to fit the fairness constraints. We then design an item classifier without item sensitive labels. The proposed framework can be trained in an end-to-end manner under most embedding based CF models. Extensive experiments conducted on three datasets (MovieLens-100K, MovieLens-1M, and Lastfm-360K) clearly demonstrate the superiority of the proposed FairCF framework relative to various fairness metrics (i.e., performance of newly-trained classifiers) than other state-of-the-art fairness-aware CF models with less than 4% accuracy reduction.

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Acknowledgements

This work was supported in part by National Natural Science Foundation of China (Grant Nos. 61972125, U19A2079, 61725203, 61732008, 62006066) and Fundamental Research Funds for the Central Universities (Grant No. JZ2020HGPA-0114). Le WU greatly thanks the support of Young Elite Scientists Sponsorship Program by CAST and ISZS.

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, China

    Pengyang Shao, Le Wu, Lei Chen, Kun Zhang & Meng Wang

  2. Intelligent Interconnected Systems Laboratory of Anhui Province, Hefei, 230601, China

    Le Wu, Kun Zhang & Meng Wang

  3. Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei, 230088, China

    Le Wu & Meng Wang

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  1. Pengyang Shao
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  2. Le Wu
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  3. Lei Chen
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  4. Kun Zhang
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  5. Meng Wang
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Correspondence to Le Wu or Meng Wang.

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Shao, P., Wu, L., Chen, L. et al. FairCF: fairness-aware collaborative filtering. Sci. China Inf. Sci. 65, 222102 (2022). https://doi.org/10.1007/s11432-020-3404-y

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