Maximum Entropy Policy for Long-Term Fairness in Interactive Recommender Systems

This article considers the problem of maintaining the long-term fairness of item exposure in interactive recommender systems under the dynamic setting that user preference and item popularity evolve over time. The challenge is that the evolving dynamics of user preference and item popularity in the feedback loop amplify the long-term "unfairness" of item exposure. To address this challenge, we first formulate a constrained Markov Decision Process (MDP) to capture the evolving dynamics of user preference. The proposed constrained MDP imposes long-term fairness requirements via maximum entropy techniques. Moreover, to illuminate the "unfairness" amplifying effect caused by the evolving dynamic of item popularity in the feedback loop, we design a debiased reward function to eliminate popularity bias in the training data. To this end, the proposed framework can maintain acceptable recommendation accuracy while exposing items as randomly as possible, ensuring long-term benefits for users. To address the data sparsity issue, the proposed framework can easily integrate self-supervised learning methods to enhance state representation. Experiments on three datasets and an authentic Reinforcement Learning environment (Virtual-Taobao) demonstrate the effectiveness and superiority of the proposed framework in terms of recommendation accuracy and fairness, and show the robustness against data sparsity and noise.