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Comparative Evaluation for Recommender Systems for Book Recommendations

dc.contributor.authorTashkandi, Araek
dc.contributor.authorWiese, Lena
dc.contributor.authorBaum, Marcus
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorNicklas, Daniela
dc.contributor.editorLeymann, Frank
dc.contributor.editorSchöning, Harald
dc.contributor.editorHerschel, Melanie
dc.contributor.editorTeubner, Jens
dc.contributor.editorHärder, Theo
dc.contributor.editorKopp, Oliver
dc.contributor.editorWieland, Matthias
dc.date.accessioned2017-06-21T11:24:40Z
dc.date.available2017-06-21T11:24:40Z
dc.date.issued2017
dc.description.abstractRecommender System (RS) technology is often used to overcome information overload. Recently, several open-source platforms have been available for the development of RSs. Thus, there is a need to estimate the predictive accuracy of such platforms to select a suitable framework. In this paper we perform an offline comparative evaluation of commonly used recommendation algorithms of collaborative filtering. They are implemented by three popular RS platforms (LensKit, Mahout, and MyMediaLite) using the BookCrossing data set containing 1,149,780 user ratings on books. Our main goal is to find out which of these RSs is the most applicable and has high performance and accuracy on these data. We consider performing a fair objective comparison by benchmarking the evaluation dimensions such as the data set and the evaluation metric. Our evaluation shows the disparity of evaluation results between the RS frameworks. This points to the need of standardizing evaluation methodologies for recommendation algorithms.en
dc.identifier.isbn978-3-88579-660-2
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW 2017) - Workshopband
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-266
dc.subjectRecommender System
dc.subjectLensKit
dc.subjectMahout
dc.subjectMyMediaLite
dc.subjectBook recommendations
dc.titleComparative Evaluation for Recommender Systems for Book Recommendationsen
dc.typeText/Conference Paper
gi.citation.endPage300
gi.citation.publisherPlaceBonn
gi.citation.startPage291
gi.conference.date6.-10. März 2017
gi.conference.locationStuttgart
gi.conference.sessiontitleStudierendenprogramm

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