A user-item relevance model for log-based collaborative filtering

Conference paper (2006)
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Published Date

2006

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Abstract

.Implicitacquisitionofuserpreferencesmakeslog-basedcollaborative¿lteringfavorableinpracticetoaccomplishrecommendations.Inthispaper,wefollowaformalapproachintextretrievaltore-formulatetheproblem.Basedontheclassicprobabilityrankingprinciple,weproposeaprobabilisticuser-itemrelevancemodel.Underthisformalmodel,weshowthatuser-basedanditem-basedapproachesareonlytwodi¿erentfactorizationswithdi¿erentindependenceassumptions.Moreover,weshowthatsmoothingisanimportantaspecttoestimatetheparametersofthemodelsduetodatasparsity.Byaddinglinearinterpolationsmoothing,theproposedmodelgivesaprobabilisticjusti¿cationofusingTF×IDF-likeitemrankingincollaborative¿ltering.Besidesgivingtheinsightunderstandingoftheproblemofcollaborative¿ltering,wealsoshowexperimentsinwhichtheproposedmethodprovidesabetterrecommendationperformanceonamusicplay-listdataset.