Fair Representation Learning for Recommendation: A Mutual Information Perspective

Abstract

Recommender systems have been widely used in recent years. By exploiting historical user-item interactions, recommender systems can model personalized potential interests of users and have been widely applied to a wide range of scenarios. Despite their impressive performance, most of them may be subject to unwanted biases related to sensitive attributes (e.g., race and gender), leading to unfairness. An intuitive idea to alleviate this problem is to ensure that there is no mutual information between recommendation results and sensitive attributes. However, keeping independence conditions solely achieves fairness improvement while causing an obvious degradation of recommendation accuracy, which is not a desired result. To this end, in this paper, we re-define recommendation fairness with a novel two-fold mutual information objective. In concerned details, we define fairness as mutual information minimization between embeddings and sensitive information, and mutual information maximization between embeddings and non-sensitive information. Then, a flexible Fair Mutual Information (FairMI) framework is designed to achieve this goal. FairMI first employs a sensitive attribute encoder to capture sensitive information in the data. Then, based on results from the sensitive attribute encoder, an interest encoder is developed to generate sensitive-free embeddings, which are expected to contain rich non-sensitive information of input data. Moreover, we propose novel mutual information (upper/lower) bounds with contrastive information estimation for model optimization. Extensive experiments over two real-world datasets demonstrate the effectiveness of our proposed FairMI in reducing unfairness and improving recommendation accuracy simultaneously.