CS Events

Qualifying Exam

Towards Fairer Recommender Systems through Deep Reinforcement Learning


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Tuesday, May 24, 2022, 10:00am - 11:30am


Speaker: Yingqiang Ge

Location : Virtual


Professor Yongfeng Zhang (Advisor)

Professor Desheng Zhang

Professor Jie Gao

Professor He Zhu

Event Type: Qualifying Exam

Abstract: The issue of fairness in recommendation is becoming increasingly essential as Recommender Systems (RS) touch and influence more and more people in their daily lives. While most of the existing fairness-aware recommendation approaches do alleviate the impact of unfair recommendations, they also have two major drawbacks. First, most of them have been situated in a static or one-shot setting, where the protected groups of items are fixed, and the model provides a one-time fairness solution based on fairness-constrained optimization, which fails to consider the dynamic nature of the recommender systems, where attributes such as item popularity may change over time due to the recommendation policy and user engagement. Second, they mainly aim at solving a constrained optimization problem by imposing a constraint on the level of fairness while optimizing the main recommendation objective, which may significantly compromise the recommendation accuracy due to the inherent trade-off between fairness and utility. Regarding to these questions, we propose two fairness-aware recommendation frameworks leveraging the advantages of deep reinforcement learning to overcome the above drawbacks correspondingly. For the first, we tackle it by proposing a fairness-constrained reinforcement learning algorithm for recommendation, which models the recommendation problem as a Constrained Markov Decision Process (CMDP), so that the model can dynamically adjust its recommendation policy to make sure the fairness requirement is always satisfied when the environment changes. For the second, we propose a fairness-aware recommendation framework using multi-objective reinforcement learning (MORL), called MoFIR (pronounced “more fair”), which is able to learn a single parametric representation for optimal recommendation policies over the space of all possible preferences between fairness and utility.


Rutgers University School of Arts & Sciences

Contact  Professor Yongfeng Zhang