Leveraging aggregate ratings for better recommendations

Akhmed Umyarov, Alexander Tuzhilin

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

10 Scopus citations


The paper presents a method that uses aggregate ratings provided by various segments of users for various categories of items to derive better estimations of unknown individual ratings. This is achieved by converting the aggregate ratings into constraints on the parameters of a rating estimation model presented in the paper. The paper also demonstrates theoretically that these additional constraints reduce rating estimation errors resulting in better rating predictions.

Original languageEnglish (US)
Title of host publicationRecSys'07
Subtitle of host publicationProceedings of the 2007 ACM Conference on Recommender Systems
Number of pages4
StatePublished - 2007
EventRecSys'07: 2007 1st ACM Conference on Recommender Systems - Minneapolis, MN, United States
Duration: Oct 19 2007Oct 20 2007

Publication series

NameRecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems


OtherRecSys'07: 2007 1st ACM Conference on Recommender Systems
Country/TerritoryUnited States
CityMinneapolis, MN


  • Aggregate ratings
  • Hierarchical Bayesian models
  • OLAP
  • Predictive models
  • Recommender systems


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