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dc.contributor.authorIgnatov, Dmitry I.
dc.contributor.authorPoelmans, Jonas
dc.contributor.authorDedene, Guido
dc.contributor.authorViaene, Stijn
dc.contributor.editorKundu, M.K.
dc.contributor.editorMitra, S.
dc.contributor.editorMazumdar, D.
dc.contributor.editorPal, S.K.
dc.date.accessioned2019-02-25T15:44:18Z
dc.date.available2019-02-25T15:44:18Z
dc.date.issued2012en_US
dc.identifier.doi10.1007/978-3-642-27387-2 25
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6153
dc.description.abstractThe topic of recommender systems is rapidly gaining interest in the user-behaviour modeling research domain. Over the years, various recommender algorithms based on different mathematical models have been introduced in the literature. Researchers interested in proposing a new recommender model or modifying an existing algorithm should take into account a variety of key performance indicators, such as execution time, recall and precision. Till date and to the best of our knowledge, no general cross-validation scheme to evaluate the performance of recommender algorithms has been developed. To fill this gap we propose an extension of conventional cross-validation. Besides splitting the initial data into training and test subsets, we also split the attribute description of the dataset into a hidden and visible part. We then discuss how such a splitting scheme can be applied in practice. Empirical validation is performed on traditional user-based and item-based recommender algorithms which were applied to the MovieLens dataset.en_US
dc.language.isoenen_US
dc.subjectRecommender Systemsen_US
dc.subjectQuality of Recommendationsen_US
dc.subjectUser-behavior Modelingen_US
dc.subjectApplied Combinatoricsen_US
dc.titleA new cross-validation technique to evaluate quality of recommender systemsen_US
dc.title.alternativePerception and machine intelligence. PerMIn 2012. Lecture notes in Computer Scienceen_US
dc.source.beginpage195en_US
dc.source.endpage202en_US
dc.contributor.departmentNational Research University Higher School of Economics (HSE), Moscow, Russiaen_US
dc.contributor.departmentKU Leuvenen_US
dc.contributor.departmentUniversiteit van Amsterdam Business School, Amsterdam, The Netherlandsen_US
vlerick.conferencedate12/01/2012-13/01/2012en_US
vlerick.conferencelocationKolkata, Indiaen_US
vlerick.conferencenameIndo-Japanese Conference on Perception and Machine Intelligenceen_US
vlerick.conferenceorganiserPerMIn 2012en_US
vlerick.knowledgedomainOperations & Supply Chain Managementen_US
vlerick.typeconfpresConference Proceedingen_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid51528en_US
dc.identifier.vperid76321en_US


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