Reinforcement Learning for Long-Term Reward Optimization in Recommender Systems

Anton Dorozhko

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

Recommender systems help users to orient in the vast space of goods, services, and events. A user interacts with the recommender engine in a sequence of exchanges of recommendations and user feedback. The idea that previous interaction influence the later ones and the importance of the sequence of interactions can be modeled using Markov decision processes and solved by reinforcement learning. Several recent articles applying reinforcement learning to recommender systems have proved the viability of this direction. But it is still difficult to compare different approaches. We propose an environment with a unified interface that will permit to compare different modelization of recommender process and different algorithms on the same underlying sequential data. We also performed the extensive parameter study for deep deterministic policy gradient methods on the well-known MovieLens dataset.

Original languageEnglish
Title of host publicationSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages862-867
Number of pages6
ISBN (Electronic)9781728144016
ISBN (Print)978-1-7281-4402-3
DOIs
Publication statusPublished - Oct 2019
Event2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019 - Novosibirsk, Russian Federation
Duration: 21 Oct 201927 Oct 2019

Publication series

NameSIBIRCON 2019 - International Multi-Conference on Engineering, Computer and Information Sciences, Proceedings

Conference

Conference2019 International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2019
CountryRussian Federation
CityNovosibirsk
Period21.10.201927.10.2019

Keywords

  • DDPG
  • deep reinforcement learning (DRL)
  • long-Term value
  • recommender systems
  • reinforcement learning

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