  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 the
recommendation problem in this setting 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.
