Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:6987-6998, 2020.
Most recommender systems (RS) research assumes that a user’s utility can be maximized independently of the utility of the other agents (e.g., other users, content providers). In realistic settings, this is often not true – the dynamics of an RS ecosystem couple the long-term utility of all agents. In this work, we explore settings in which content providers cannot remain viable unless they receive a certain level of user engagement. We formulate this problem as one of equilibrium selection in the induced dynamical system, and show that it can be solved as an optimal constrained matching problem. Our model ensures the system reaches an equilibrium with maximal social welfare supported by a sufficiently diverse set of viable providers. We demonstrate that even in a simple, stylized dynamical RS model, the standard myopic approach to recommendation - always matching a user to the best provider - performs poorly. We develop several scalable techniques to solve the matching problem, and also draw connections to various notions of user regret and fairness, arguing that these outcomes are fairer in a utilitarian sense.