Leveraging aggregate ratings for improving predictive performance of recommender systems

Akhmed Umyarov

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

Abstract

One of the key problems in recommender systems is accurate estimation of unknown ratings of individual items for individual users in terms of the previously specified ratings and other characteristics of items and users. In this thesis, we investigate a way of improving estimations of individual ratings using externally provided properties of aggregate ratings for groups of items and users, such as an externally specified average rating of action movies provided by graduate students or externally specified standard deviation of ratings for comedy movies.

Original languageEnglish (US)
Title of host publicationRecSys'08
Subtitle of host publicationProceedings of the 2008 ACM Conference on Recommender Systems
Pages327-330
Number of pages4
DOIs
StatePublished - Dec 1 2008
Event2008 2nd ACM International Conference on Recommender Systems, RecSys'08 - Lausanne, Switzerland
Duration: Oct 23 2008Oct 25 2008

Publication series

NameRecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems

Other

Other2008 2nd ACM International Conference on Recommender Systems, RecSys'08
CountrySwitzerland
CityLausanne
Period10/23/0810/25/08

Keywords

  • Aggregate ratings
  • Collaborative filtering
  • Hierarchical models
  • Predictive models
  • Recommender systems

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