Recommendation Fairness: From Static to Dynamic
This work tackles fairness issues in recommender systems for users and developers, but it is incremental as it builds on existing trends without presenting new empirical results.
The paper addresses the need to transition fairness research in recommender systems from static to dynamic approaches, proposing to integrate fairness into reinforcement learning techniques and suggesting future directions like multi-agent and multi-objective optimization within a stochastic games framework.
Driven by the need to capture users' evolving interests and optimize their long-term experiences, more and more recommender systems have started to model recommendation as a Markov decision process and employ reinforcement learning to address the problem. Shouldn't research on the fairness of recommender systems follow the same trend from static evaluation and one-shot intervention to dynamic monitoring and non-stop control? In this paper, we portray the recent developments in recommender systems first and then discuss how fairness could be baked into the reinforcement learning techniques for recommendation. Moreover, we argue that in order to make further progress in recommendation fairness, we may want to consider multi-agent (game-theoretic) optimization, multi-objective (Pareto) optimization, and simulation-based optimization, in the general framework of stochastic games.