Learning Fair Representations for Recommendation via Information Bottleneck Principle

Abstract

User-oriented recommender systems (RS) characterize users' preferences based on observed behaviors and are widely deployed in personalized services. However, RS may unintentionally capture biases related to sensitive attributes (e.g., gender) from behavioral data, leading to unfair issues and discrimination against particular groups (e.g., females). Adversarial training is a popular technique for fairness-aware RS, when filtering sensitive information in user modeling. Despite advancements in fairness, achieving a good accuracy-fairness trade-off remains a challenge in adversarial training. In this paper, we investigate fair representation learning from a novel information theory perspective. Specifically, we propose a model-agnostic Fair recommendation method via the Information Bottleneck principle FairIB. The learning objective of FairIB is to maximize the mutual information between user representations and observed interactions, while simultaneously minimizing it between user representations and sensitive attributes. This approach facilitates the capturing of essential collaborative signals in user representations while mitigating the inclusion of unnecessary sensitive information. Empirical studies on two real-world datasets demonstrate the effectiveness of the proposed FairIB, which significantly improves fairness while maintaining competitive recommendation accuracy, either in single or multiple sensitive scenarios. The code is available at https://github.com/jsxie9/IJCAI_FairIB.