Learning with Exposure Constraints in Recommendation Systems

Omer Ben-Porat, Rotem Torkan

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

Abstract

Recommendation systems are dynamic economic systems that balance the needs of multiple stakeholders. A recent line of work studies incentives from the content providers' point of view. Content providers, e.g., vloggers and bloggers, contribute fresh content and rely on user engagement to create revenue and finance their operations. In this work, we propose a contextual multi-armed bandit setting to model the dependency of content providers on exposure. In our model, the system receives a user context in every round and has to select one of the arms. Every arm is a content provider who must receive a minimum number of pulls every fixed time period (e.g., a month) to remain viable in later rounds; otherwise, the arm departs and is no longer available. The system aims to maximize the users' (content consumers) welfare. To that end, it should learn which arms are vital and ensure they remain viable by subsidizing arm pulls if needed. We develop algorithms with sub-linear regret, as well as a lower bound that demonstrates that our algorithms are optimal up to logarithmic factors.

Original languageEnglish
Title of host publicationACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
Pages3456-3466
Number of pages11
ISBN (Electronic)9781450394161
DOIs
StatePublished - 30 Apr 2023
Event2023 World Wide Web Conference, WWW 2023 - Austin, United States
Duration: 30 Apr 20234 May 2023

Publication series

NameACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023

Conference

Conference2023 World Wide Web Conference, WWW 2023
Country/TerritoryUnited States
CityAustin
Period30/04/234/05/23

Keywords

  • economics and computation
  • multi-armed bandits

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

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