Fairness-Aware Tensor-Based Recommendation
Fairness-Aware Tensor-Based Recommendation
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DOI：
10.1145/3269206.3271795
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发表时间：
2018-10
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通讯作者：
Ziwei Zhu;Xia Hu;James Caverlee
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文献类型：
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作者：
Ziwei Zhu;Xia Hu;James Caverlee
基于张量的方法在改进推荐系统的传统矩阵分解方法方面已显示出潜力。但是，张量可能会在提高推荐质量的同时降低推荐的公平性。因此，我们提出了一种新颖的具有公平意识的张量推荐框架，该框架旨在保持质量的同时大幅提高公平性。所提出框架的四个关键方面是：（i）一个用于隔离敏感特征的新的敏感潜在因子矩阵；（ii）一个敏感信息正则化器，它提取可能污染其他潜在因子的敏感信息；（iii）一种求解所提出的优化模型的有效算法；以及（iv）对先前研究未涉及的多特征和多类别情况的扩展。在真实世界和合成数据集上进行的大量实验表明，与最先进的替代方法相比，该框架在保持推荐质量的同时提高了推荐的公平性。
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.