Returning is believing: Optimizing long-term user engagement in recommender systems

Qingyun Wu, Hongning Wang, Liangjie Hong, Yue Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations

Abstract

In this work, we propose to improve long-term user engagement in a recommender system from the perspective of sequential decision optimization, where users' click and return behaviors are directly modeled for online optimization. A bandit-based solution is formulated to balance three competing factors during online learning, including exploitation for immediate click, exploitation for expected future clicks, and exploration of unknowns for model estimation. We rigorously prove that with a high probability our proposed solution achieves a sublinear upper regret bound in maximizing cumulative clicks from a population of users in a given period of time, while a linear regret is inevitable if a user's temporal return behavior is not considered when making the recommendations. Extensive experimentation on both simulations and a large-scale real-world dataset collected from Yahoo frontpage news recommendation log verified the effectiveness and significant improvement of our proposed algorithm compared with several state-of-the-art online learning baselines for recommendation.

Original languageEnglish (US)
Title of host publicationCIKM 2017 - Proceedings of the 2017 ACM Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1927-1936
Number of pages10
ISBN (Electronic)9781450349185
DOIs
StatePublished - Nov 6 2017
Event26th ACM International Conference on Information and Knowledge Management, CIKM 2017 - Singapore, Singapore
Duration: Nov 6 2017Nov 10 2017

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings
VolumePart F131841

Other

Other26th ACM International Conference on Information and Knowledge Management, CIKM 2017
Country/TerritorySingapore
CitySingapore
Period11/6/1711/10/17

All Science Journal Classification (ASJC) codes

  • Decision Sciences(all)
  • Business, Management and Accounting(all)

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