时间感知动态推荐

Time-AwareDynamicRecommendationTime-AwareCollaborativeFilteringforRecommenderSystems

ProductrecommendationwithtemporaldynamicscrucialcontentProposeatime-awarecollaborativefilteringalgorithmwhichtracksuserinterestsanditempopularityovertimeExtendthewidelyusedneighborhoodbasedalgorithmsbyincorporatingtwokindsoftemporalinformationanddevelopanimprovedalgorithmformakingtimelyrecommendationsTimeWeightBasedonUserInterestMorerecentdatashouldhavehighervalueinthetimeweighting.Soweproposethateachratingofitemiassignedbyusersuisassignedaweightdefinedbyafunctionfu_s(tui)totherecenttime.tui

denotesthedistancebetweenthetimewhentheuseruratedonitemiandthetimewhentheuseruvisitedoneiteminthefirsttime.TheearlytimeweightfunctionofuserinterestLinearcombination

TimeWeightBasedonItemPopularityIntherecommendationsystem,theitempopularitywouldchangeovertime.Where,thevalueofexp(-λti)isreducingwithtiincreasing,whichshowsthebasiccharacteristicofnewproductpopularityreducingovertime.Besides,ifkij=1thentheitemiissensitivetotheseasonjelsekij=0.SeasonaleffectAnImprovedTimeWeightCFTomeasureitemsimilaritiesbyincorporatingtemporalinformation,wemodifythewellknowncosinesimilarityasfollows:

timeweightcollaborativefilteringalgorithmExperimentsDatasetPreparingselect475,154userson3,856kindsofproductions,totally9,548,825purchaserecords:80%ofdatasetispracticingset,remaining20%istestingsetEvaluationProtocolBaselinesItem-BasedCFalgorithm(IBCF)User-BasedCFalgorithm(UBCF)TimeWeightCollaborativeFiltering

ComparisonofdifferenttimefunctionsProductrecommendationwithtemporaldynamicsthecontributionisthreefold:Wedefineanewclassofrecommendationproblem,namedrecommendationwithstage(RwS),inwhichauser’spreferenceevolvesovertime.Weintroduceataxonomy-orientedapproachtomodelauser’slong-termpreference.

Weproposetocaptureauser’spreferencenotonlyontheitemlevel,butalsoonthesemanticcorrelationsamongtheconceptsorcategoriesthattheitemsbelongto.Tolocalizeauser’sspecificinterestwithinastage,weempiricallysegmenttheuser’sconsumptionhistory,andthenputmorefocusontherecentorcurrentstageinordertoprovidetheuserareasonablerecommendationlist.ProblemformulationProblemstatementProblem(recommendationwithstage):WithinanE-commercecommunity,givenU,P,RandT,recommenditemstoatargetuser,guaranteeingthattheuser’scurrentpurchasingdesireismaximallysatisfied.Taxonomy-drivenrecommendationStageidentification

Userprofilegenerationc4toc1:wethentransfertheratingscoreonp41tothecategorypath,i.e.,assigning‘‘1’’toeachcategoryinthepath.Similarityrefinement

User–usersimilarity(SU):twodifferentcomponents:user–itemsimilaritySUIanduser–categorysimilaritySUC.Item–itemsimilarity(SI):Category–categorysimilarity(SC):employInformationContent(IC)(Resnik)tocomputeSC.ItemrecommendationAfterwelocatetherecommendationstageofthecustomer,weinvestigateGraph-basedmodelforrecommendationa.wefirstconstructamulti-modalgraph,andthengivenatargetcustomeru,weperformrandomwalkonthisgraphstartingfromuforrecommendation.b.Weencapsulethegraphasana