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REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale

Authors:

Richard Liaw,

Paige Bailey,

Ying Li,

Maria Dimakopoulou,

Yves RaimondAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 684 - 685

https://doi.org/10.1145/3523227.3547418

Published: 13 September 2022 Publication History

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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?

Index Terms

REVEAL 2022: Reinforcement Learning-Based Recommender Systems at Scale
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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Information

Published In

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 September 2022

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Introduction
Research
Refereed limited

Conference

RecSys '22

Sponsor:

RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Index Terms

Recommendations

Reinforcement Learning based Recommender Systems: A Survey

REVEAL 2020: Bandit and Reinforcement Learning from User Interactions

Reinforcement learning for addressing the cold-user problem in recommender systems