Retention Depolarization in Recommender System

Repeated risk minimization is a popular choice in real-world recommender systems driving their recommendation algorithms to adapt to user preferences and trends. However, numerous studies have shown that it exacerbates retention disparities among user groups, resulting in polarization within the user population. Given the primary objective of improving long-term user engagement in most industrial recommender systems and the significant commercial benefits from a diverse user population, enforcing retention fairness across user population is therefore crucial. Nonetheless, this goal is highly challenging due to the unknown dynamics of user retention (e.g., when a user would abandon the system) and the simultaneous aim to maximize the experience of every user. In this paper, we propose ReFair, the first computational framework that continuously improves recommendation algorithms while ensuring long-term retention fairness in the entire user population. ReFair alternates between environment learning (i.e., estimate the user retention dynamics) and fairness constrained policy improvement with respect to the estimated environment, while effectively handling uncertainties in the estimation. Our solution provides strong theoretical guarantees for long-term recommendation performance and retention fairness violation. Empirical experiments on two real-world recommendation datasets also demonstrate its effectiveness in realizing these two goals.