Leveraging Aggregate Ratings for Better Recommendations

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DC Field	Value	Language
dc.contributor.author	Umyarov, Akhmed	-
dc.contributor.author	Tuzhilin, Alexander	-
dc.date.accessioned	2007-09-13T20:59:07Z	-
dc.date.available	2007-09-13T20:59:07Z	-
dc.date.issued	2007-09-13T20:59:07Z	-
dc.identifier.uri	http://hdl.handle.net/2451/23402	-
dc.description.abstract	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.	en
dc.format.extent	211359 bytes	-
dc.format.mimetype	application/pdf	-
dc.relation.ispartofseries	CeDER-07-03	en
dc.subject	Recommender systems	en
dc.subject	Hierarchical Bayesian models	en
dc.subject	predictive models	en
dc.subject	aggregate ratings	en
dc.subject	OLAP	en
dc.title	Leveraging Aggregate Ratings for Better Recommendations	en
Appears in Collections:	CeDER Working Papers IOMS: Information Systems Working Papers

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