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arXiv · 2204.11421

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

Akos Lada·Xiaoxuan Liu·Jens Rischbieth·Yi Wang·Yuwen Zhang
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Abstract

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.

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Akos Lada, Xiaoxuan Liu, Jens Rischbieth, Yi Wang, Yuwen Zhang. 2022-04-25. Long-run User Value Optimization in Recommender Systems through Content Creation Modeling. https://arxiv.org/abs/2204.11421

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