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Optimizing Long-term Social Welfare in Recommender Systems: A Constrained Matching Approach
Martin Mladenov · Elliot Creager · Omer Ben-Porat · Kevin Swersky · Richard Zemel · Craig Boutilier

Tue Jul 14 07:00 AM -- 07:45 AM & Tue Jul 14 07:00 PM -- 07:45 PM (PDT) @

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.

Author Information

Martin Mladenov (Google)
Elliot Creager (University of Toronto)
Omer Ben-Porat (Technion--Israel Institute of Technology)
Kevin Swersky (Google Brain)
Richard Zemel (Vector Institute)
Craig Boutilier (Google)

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