Recommendation systems have been used in many domains, and in recent years, ethical problems associated with such systems have gained serious attention. The problem of unfairness in friendship or link recommendation systems in social networks has begun attracting attention, as such unfairness can cause problems like segmentation and echo chambers. One challenge in this problem is that there are many fairness metrics for networks, and existing methods only consider the improvement of a single specific fairness indicator.
In this work, we model the fair link prediction problem as a multi-armed bandit problem. We propose FairLink, a multi-armed bandit based framework that predicts new edges that are both accurate and well-behaved with respect to a fairness property of choice. This method allows the user to specify the desired fairness metric. Experiments on five real-world datasets show that FairLink can achieve a significant fairness improvement as compared to a standard recommendation algorithm, with only a small reduction in accuracy.
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Fairness-Aware Tensor-Based Recommendation
Tensor-based methods have shown promise in improving upon traditional matrix factorization methods for recommender systems. But tensors may achieve improved recommendation quality while worsening the fairness of the recommendations. Hence, we propose a novel fairness-aware tensor recommendation framework that is designed to maintain quality while dramatically improving fairness. Four key aspects of the proposed framework are: (i) a new sensitive latent factor matrix for isolating sensitive features; (ii) a sensitive information regularizer that extracts sensitive information which can taint other latent factors; (iii) an effective algorithm to solve the proposed optimization model; and (iv) extension to multi-feature and multi-category cases which previous efforts have not addressed. Extensive experiments on real-world and synthetic datasets show that the framework enhances recommendation fairness while preserving recommendation quality in comparison with state-of-the-art alternatives.
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- Award ID(s):
- 1841138
- PAR ID:
- 10098220
- Date Published:
- Journal Name:
- Proceedings of the 27th ACM International Conference on Information and Knowledge Management
- Page Range / eLocation ID:
- 1153 to 1162
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3269206.3271795
