REVEAL 2019: closing the loop with the real world: reinforcement and robust estimators for recommendation

Abstract

The REVEAL workshop1 focuses on framing the recommendation problem as a one of making personalized interventions. Moreover, these interventions sometimes depend on each other, where a stream of interactions occurs between the user and the system, and where each decision to recommend something will have an impact on future steps and long-term rewards. This framing creates a number of challenges we will discuss at the workshop. How can recommender systems be evaluated offline in such a context? How can we learn recommendation policies that are aware of these delayed consequences and outcomes?