Preference elicitation for narrowing the recommended list for groups

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

10 Scopus citations

Abstract

A group may appreciate recommendations on items that fit their joint preferences. When the members' actual preferences are unknown, a recommendation can be made with the aid of collaborative filtering methods. We offer to narrow down the recommended list of items by eliciting the users' actual preferences. Our final goal is to output top-N preferred items to the group out of the top-N recommendations provided by the recommender system (K < N), where one of the items is a necessary winner. We propose an iterative preference elicitation method, where users are required to provide item ratings per request. We suggest a heuristic that attempts to minimize the preference elicitation effort under two aggregation strategies. We evaluate our methods on real-world Netflix data as well as on simulated data which allows us to study different cases. We show that preference elicitation effort can be cut in up to 90% while preserving the most preferred items in the narrowed list.

Original languageEnglish
Title of host publicationRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems
PublisherAssociation for Computing Machinery
Pages333-336
Number of pages4
ISBN (Electronic)9781450326681
DOIs
StatePublished - 6 Oct 2014
Event8th ACM Conference on Recommender Systems, RecSys 2014 - Foster City, United States
Duration: 6 Oct 201410 Oct 2014

Publication series

NameRecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems

Conference

Conference8th ACM Conference on Recommender Systems, RecSys 2014
Country/TerritoryUnited States
CityFoster City
Period6/10/1410/10/14

Keywords

  • Group recommender systems
  • Preference elicitation

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