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dc.contributor.authorUmyarov, Akhmed-
dc.contributor.authorTuzhilin, Alexander-
dc.date.accessioned2009-05-27T14:14:00Z-
dc.date.available2009-05-27T14:14:00Z-
dc.date.issued2009-05-27T14:14:00Z-
dc.identifier.urihttp://hdl.handle.net/2451/28089-
dc.description.abstractThis paper describes an approach for incorporating externally specified aggregate ratings information into certain types of recommender systems, including two types of collaborating filtering and a hierarchical linear regression model. First, we present a framework for incorporating aggregate rating information and apply this framework to the aforementioned individual rating models. Then we formally show that this additional aggregate rating information provides more accurate recommendations of individual items to individual users. Further, we experimentally confirm this theoretical finding by demonstrating on several datasets that the aggregate rating information indeed leads to better predictions of unknown ratings. We also propose scalable methods for incorporating this aggregate information and test our approaches on large datasets. Finally, we demonstrate that the aggregate rating information can also be used as a solution to the cold start problem of recommender systems.en
dc.description.sponsorshipNYU, Stern School of Business, Center for Digital Economy Researchen
dc.format.extent415049 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen_USen
dc.relation.ispartofseriesCeDER-08-03en
dc.subjectRecommender systemsen
dc.subjectcollaborative filteringen
dc.subjecthierarchical linear modelsen
dc.subjectpredictive modelsen
dc.subjectaggregate ratingsen
dc.subjectcold-stat problemen
dc.titleLeveraging aggregate ratings for improving predictive performance of recommender systemsen
dc.typeArticleen
Appears in Collections:CeDER Working Papers

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