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dc.contributor.authorPanniello, Umberto-
dc.contributor.authorTuzhilin, Alexander-
dc.contributor.authorGorgoglione, Michele-
dc.date.accessioned2012-10-18T13:38:28Z-
dc.date.available2012-10-18T13:38:28Z-
dc.date.issued2012-10-18T13:38:28Z-
dc.identifier.urihttp://hdl.handle.net/2451/31636-
dc.description.abstractAlthough the area of Context-Aware Recommender Systems (CARS) has made a significant progress over the last several years, the problem of comparing various contextual pre-filtering, post-filtering and contextual modeling methods remained fairly unexplored. In this paper, we address this problem and compare several contextual pre-filtering, post-filtering and contextual modeling methods in terms of the accuracy and diversity of their recommendations to determine which methods outperform the others and under which circumstances. To this end, we consider three major factors affecting performance of CARS methods, such as the type of the recommendation task, context granularity and the type of the recommendation data. We show that none of the considered CARS methods uniformly dominates the others across all of these factors and other experimental settings; but that a certain group of contextual modeling methods constitutes a reliable “best bet” when choosing a sound CARS approach since they provide a good balance of accuracy and diversity of contextual recommendations.en
dc.description.sponsorshipPolitecnico di Bari, Italy; NYU Stern School of Businessen
dc.language.isoen_USen
dc.relation.ispartofseriesCBA-12-01-
dc.subjectContext-aware recommender systems, CARS, pre-filtering, post-filtering, contextual modeling, accuracy, diversity, performance measures.en
dc.titleComparing Context-Aware Recommender Systems in Terms of Accuracy and Diversity: Which Contextual Modeling, Pre-filtering and Post-Filtering Methods Perform the Besten
dc.typeWorking Paperen
dc.authorid-ssrn1131950en
Appears in Collections:Center for Business Analytics Working Papers

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