Abstract: One embodiment sets forth a method for generating a trained recommendation model. According to some embodiments, the method can be implemented by a computing device, and includes the steps of receiving user interaction data for a plurality of users; generating a plurality of user cohorts based on the user interaction data; assigning, to each user cohort in the plurality of user cohorts, a personalized proxy reward function; generating, for each user cohort included in the plurality of user cohorts, an expected long-term reward for the user cohort based on the personalized proxy reward function; and for each user cohort included in the plurality of user cohorts, updating the personalized proxy reward function based on the expected long-term reward for the user cohort, and generating a recommendation policy based on the personalized proxy reward function for the user cohort.

Abstract: A computer-implemented method includes identifying offline evaluation metrics that indicate, for a given feedback loop in a recommendation system, various feedback loop characteristics that are detrimental to the feedback loop. The method also includes generating a predictive machine learning (ML) model that correlates the identified offline evaluation metrics with indications of those feedback loop characteristics that are detrimental to the feedback loop. The method further includes instantiating the predictive ML model to predict, using the correlated offline evaluation metrics and the detrimental feedback loop characteristics, how the feedback loop will be negatively affected over time, and providing, to at least one entity, an indication of how the feedback loop will be negatively affected over time due to the detrimental feedback loop characteristics. Various other methods, systems, and computer-readable media are also disclosed.