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

Long-run User Value Optimization in Recommender Systems through Content Creation Modeling