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dc.contributor.authorSymeonidis P
dc.contributor.authorNanopoulos A
dc.contributor.authorPapadopoulos A
dc.contributor.authorManolopoulos Y
dc.contributor.editor
dc.date.accessioned2019-03-08T08:15:49Z
dc.date.available2019-03-08T08:15:49Z
dc.date.issued2008
dc.identifier.issn0957-4174
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2007.05.013
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0957417407001959
dc.identifier.urihttp://hdl.handle.net/10863/9042
dc.description.abstractRecommender systems base their operation on past user ratings over a collection of items, for instance, books, CDs, etc. Collaborative filtering (CF) is a successful recommendation technique that confronts the "information overload" problem. Memory-based algorithms recommend according to the preferences of nearest neighbors, and model-based algorithms recommend by first developing a model of user ratings. In this paper, we bring to surface factors that affect CF process in order to identify existing false beliefs. In terms of accuracy, by being able to view the "big picture", we propose new approaches that substantially improve the performance of CF algorithms. For instance, we obtain more than 40% increase in precision in comparison to widely-used CF algorithms. In terms of efficiency, we propose a model-based approach based on latent semantic indexing (LSI), that reduces execution times at least 50% than the classic CF algorithms. © 2007 Elsevier Ltd. All rights reserved.en_US
dc.languageEnglish
dc.language.isoenen_US
dc.relation
dc.rights
dc.subjectNearest neighborsen_US
dc.subjectCollaborative filtering (CF)en_US
dc.subjectRecommender systemsen_US
dc.titleCollaborative recommender systems: Combining effectiveness and efficiencyen_US
dc.typeArticleen_US
dc.date.updated2019-03-08T03:01:28Z
dc.publication.title
dc.language.isiEN-GB
dc.journal.titleExpert Systems with Applications
dc.description.fulltextopenen_US


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