  Content recommender systems are generally adept at maximizing immediate user
satisfaction but to optimize for the \textit{long-run} user value, we need more
statistically sophisticated solutions than off-the-shelf simple recommender
algorithms. In this paper we lay out such a solution to optimize
\textit{long-run} user value through discounted utility maximization and a
machine learning method we have developed for estimating it. Our method
estimates which content producers are most likely to create the highest
long-run user value if their content is shown more to users who enjoy it in the
present. We do this estimation with the help of an A/B test and heterogeneous
effects machine learning model. We have used such models in Facebook's feed
ranking system, and such a model can be used in other recommender systems.
