Making aggregate-level predictions in recommender systems using multi-level ratings

Akhmed Umyarov, Alexander Tuzhilin

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

Abstract

Aggregate-level ratings have been studied in recommender systems and have been shown to improve predictions of ratings of individual items for individual users. Similarly, individual-level ratings have also been used for the estimation of aggregate-level ratings for groups of items and users. In this paper, we combine these approaches and present a novel method for estimating unknown aggregate-level ratings from the known individual- And the aggregate-level ratings corresponding to different levels of the rating aggregation hierarchy. We show both theoretically and empirically that this combined approach outperforms the alternative methods that do not include the ratings from different levels of the rating aggregation hierarchy.

Original languageEnglish (US)
Title of host publication19th Workshop on Information Technologies and Systems, WITS 2009
PublisherSocial Science Research Network
Pages211-216
Number of pages6
StatePublished - Jan 1 2009
Event19th Workshop on Information Technologies and Systems, WITS 2009 - Phoenix, AZ, United States
Duration: Dec 14 2009Dec 15 2009

Other

Other19th Workshop on Information Technologies and Systems, WITS 2009
CountryUnited States
CityPhoenix, AZ
Period12/14/0912/15/09

Keywords

  • Aggregate ratings
  • Predictive models
  • Recommender systems

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