Research Note—The Halo Effect in Multicomponent Ratings and Its Implications for Recommender Systems: The Case of Yahoo! Movies

Material TypeArticleLanguageEnglish
TitleResearch Note—The Halo Effect in Multicomponent Ratings and Its Implications for Recommender Systems: The Case of Yahoo! Movies Author(S)Nachiketa Sahoo (Author)
Abstract  Collaborative filtering algorithms learn from the ratings of a group of users on a set of items to find personalized recommendations for each user. Traditionally they have been designed to work with one-dimensional ratings. With interest growing in recommendations based on multiple aspects of items, we present an algorithm for using multicomponent rating data. The presented mixture model-based algorithm uses the component rating dependency structure discovered by a structure learning algorithm. The structure is supported by the psychometric literature on the halo effect ...Paginationp 231-246
SubjectManagementDescriptorsInformation technology Computers
Journal TitleInformation Systems Research  
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