Quantifying the multi-user account problem for collaborative filtering based recommender systems

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dc.contributor.advisorGhosh, Joydeep
dc.contributor.committeeMemberGhosh, Joydeepen
dc.contributor.committeeMemberDhillon, Inderjiten
dc.creatorEdwards, James Adrianen
dc.date.accessioned2010-09-15T20:59:09Zen
dc.date.accessioned2010-09-15T20:59:15Zen
dc.date.accessioned2017-05-11T22:20:12Z
dc.date.available2010-09-15T20:59:09Zen
dc.date.available2010-09-15T20:59:15Zen
dc.date.available2017-05-11T22:20:12Z
dc.date.issued2009-12en
dc.date.submittedDecember 2009en
dc.date.updated2010-09-15T20:59:15Zen
dc.descriptiontexten
dc.description.abstractIdentification based recommender systems make no distinction between users and accounts; all the data collected during account sessions are attributed to a single user. In reality this is not necessarily true for all accounts; several different users who have distinct, and possibly very different, preferences may access the same account. Such accounts are identified as multi-user accounts. Strangely, no serious study considering the existence of multi-user accounts in recommender systems has been undertaken. This report quantifies the affect multi-user accounts have on the predictive capabilities of recommender system, focusing on two popular collaborative filtering algorithms, the kNN user-based and item-based models. The results indicate that while the item-based model is largely resistant to multi-user account corruption the quality of predictions generated by the user-based model is significantly degraded.en
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2009-12-460en
dc.language.isoengen
dc.subjectRecommender systemsen
dc.subjectCollaborative filteringen
dc.subjectMulti-user accountsen
dc.titleQuantifying the multi-user account problem for collaborative filtering based recommender systemsen
dc.type.genrethesisen

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