Collaborative Filtering
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Transcript of Collaborative Filtering
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Collaborativefiltering
Collaborativefiltering(CF)isatechniqueusedbysomerecommendersystems.Collaborativefilteringhastwosenses,anarrowoneandamoregeneralone.[2]Ingeneral,collaborativefilteringistheprocessoffilteringforinformationorpatternsusingtechniquesinvolvingcollaborationamongmultipleagents,viewpoints,datasources,etc.Applicationsofcollaborativefilteringtypicallyinvolveverylargedatasets.Collaborativefilteringmethodshavebeenappliedtomanydifferentkindsofdataincluding:sensingandmonitoringdata,suchasinmineralexploration,environmentalsensingoverlargeareasormultiplesensorsfinancialdata,suchasfinancialserviceinstitutionsthatintegratemanyfinancialsourcesorinelectroniccommerceandwebapplicationswherethefocusisonuserdata,etc.Theremainderofthisdiscussionfocusesoncollaborativefilteringforuserdata,althoughsomeofthemethodsandapproachesmayapplytotheothermajorapplicationsaswell.Inthenewer,narrowersense,collaborativefilteringisamethodofmakingautomaticpredictions(filtering)abouttheinterestsofauserbycollectingpreferencesortasteinformationfrommanyusers(collaborating).TheunderlyingassumptionofthecollaborativefilteringapproachisthatifapersonAhasthesameopinionasapersonBonanissue,AismorelikelytohaveB'sopiniononadifferentissuexthantohavetheopiniononxofapersonchosenrandomly.Forexample,acollaborativefilteringrecommendationsystemfortelevisiontastescouldmakepredictionsaboutwhichtelevisionshowausershouldlikegivenapartiallistofthatuser'stastes(likesordislikes).Notethatthesepredictionsarespecifictotheuser,butuseinformationgleanedfrommanyusers.Thisdiffersfromthesimplerapproachofgivinganaverage(nonspecific)scoreforeachitemofinterest,forexamplebasedonitsnumberofvotes.ThegrowthoftheInternethasmadeitmuchmoredifficulttoeffectivelyextractusefulinformationfromalltheavailableonlineinformation.Theoverwhelmingamountofdatanecessitatesmechanismsforefficientinformationfiltering.Oneofthetechniquesusedfordealingwiththisproblemiscalledcollaborativefiltering.Themotivationforcollaborativefilteringcomesfromtheideathatpeopleoftengetthebestrecommendationsfromsomeonewithsimilartastestothemselves.Collaborativefilteringexplorestechniquesformatchingpeoplewithsimilarinterestsandmakingrecommendationsonthisbasis.Collaborativefilteringalgorithmsoftenrequire(1)usersactiveparticipation,(2)aneasywaytorepresentusersintereststothesystem,and(3)algorithmsthatareabletomatchpeoplewithsimilarinterests.Typically,theworkflowofacollaborativefilteringsystemis:
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a. Auserexpresseshisorherpreferencesbyratingitems(e.g.books,moviesorCDs)ofthesystem.Theseratingscanbeviewedasanapproximaterepresentationoftheuser'sinterestinthecorrespondingdomain.
b. Thesystemmatchesthisusersratingsagainstotherusersandfindsthepeoplewithmostsimilartastes.
c. Withsimilarusers,thesystemrecommendsitemsthatthesimilarusershaveratedhighlybutnotyetbeingratedbythisuser(presumablytheabsenceofratingisoftenconsideredastheunfamiliarityofanitem)
d. Akeyproblemofcollaborativefilteringishowtocombineandweightthepreferencesofuserneighbors.Sometimes,userscanimmediatelyratetherecommendeditems.
Asaresult,thesystemgainsanincreasinglyaccuraterepresentationofuserpreferencesovertime.