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dc.contributor.advisorGhosh, Joydeepen
dc.creatorSudan, Nikita Mapleen
dc.date.accessioned2011-09-30T17:45:58Zen
dc.date.available2011-09-30T17:45:58Zen
dc.date.issued2011-08en
dc.date.submittedAugust 2011en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2011-08-3855en
dc.descriptiontexten
dc.description.abstractRecommender Systems are used to select online information relevant to a given user. Traditional (memory based) recommenders explore the user-item rating matrix and make recommendations based on users who have rated similarly or items that have been rated similarly. With the growing popularity of social networks, recommender systems can benefit from combining history of user preferences with information from the social/trust network of users. This thesis explores two techniques of combining user-item rating history with trust network information to make better user-item rating predictions. The first approach (SCOAL [5]) simultaneously co-clusters and learns separate models for each co-cluster. The co-clustering is based on the user features as well as the rating history. This captures the intuition that certain groups of users have similar preferences for certain groups of items. The grouping of certain users is affected by the similarity in the rating behavior and the trust network. The second graph-based label propagation approach (MAD [27]) works in a transductive setting and propagates ratings of user-item pairs directly on the user social graph. We evaluate both approaches on two large public data-sets from Epinions.com and Flixster.com. The thesis is amongst the first to explore the role of distrust in rating prediction. Since distrust is not as transitive as trust i.e. an enemy's enemy need not be an enemy or a friend, distrust can't directly replace trust in trust propagation approaches. By using a low dimensional representation of the original trust network in SCOAL, we use distrust as it is and don't propagate it. Using SCOAL, we can pin-point the groups of users and the groups of items that have the same preference model. Both SCOAL and MAD are able to seamlessly integrate side information such as item-subject and item-author information into the trust based rating prediction model.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectSocial networksen
dc.subjectTrusten
dc.subjectRecommender systemsen
dc.subjectRating predictionen
dc.subjectCo-clusteringen
dc.subjectLabel propagationen
dc.titleUsing social network information in recommender systemsen
dc.date.updated2011-09-30T17:46:06Zen
dc.identifier.slug2152/ETD-UT-2011-08-3855en
dc.contributor.committeeMemberBaldridge, Jasonen
dc.description.departmentElectrical and Computer Engineeringen
dc.type.genrethesisen
thesis.degree.departmentElectrical and Computer Engineeringen
thesis.degree.disciplineElectrical and Computer Engineeringen
thesis.degree.grantorUniversity of Texas at Austinen
thesis.degree.levelMastersen
thesis.degree.nameMaster of Science in Engineeringen


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