REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale

Abstract

Recommendation systems are increasingly modelled as a sequential decision making process, where the system decides which items to recommend to a given user. Each decision to recommend an item or slate of items has a significant impact on immediate and future user responses, long-term satisfaction or engagement with the system, and possibly valuable exposure for the item provider. The REVEAL workshop will focus on how to optimise this multi-step decision-making process, where a stream of interactions occurs between the user and the system. Deriving reward signals from these interactions, and creating a scalable, performant, and maintainable recommendation model to use for inference is a key challenge for machine learning teams, both in industry and academia. We will discuss the following challenges at the workshop: How can recommendation system models take into account the delayed effects of each recommendation? What are the right ways to reason and plan for longer-term user satisfaction? How can we leverage techniques such as Reinforcement Learning (RL) at scale?