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dc.contributor.advisorGhosh, Joydeepen
dc.contributor.committeeMemberSanghavi, Sujayen
dc.creatorGunasekar, Suriyaen
dc.date.accessioned2012-08-13T14:27:01Zen
dc.date.accessioned2017-05-11T22:26:48Z
dc.date.available2012-08-13T14:27:01Zen
dc.date.available2017-05-11T22:26:48Z
dc.date.issued2012-05en
dc.date.submittedMay 2012en
dc.identifier.urihttp://hdl.handle.net/2152/ETD-UT-2012-05-5740en
dc.descriptiontexten
dc.description.abstractThis report presents a survey of the state-of-the-art methods for building recommendation systems. The report mainly concentrates on systems that use the available side information in addition to a fraction of known affinity values such as ratings. Such data is referred to as Dyadic Data with Covariates (DyadC). The sources of side information being considered includes user/item entity attributes, temporal information and social network attributes. Further, two new models for recommendation systems that make use of the available side information within the collaborative filtering (CF) framework, are proposed. Review Quality Aware Collaborative Filtering, uses external side information, especially review text to evaluate the quality of available ratings. These quality scores are then incorporated into probabilistic matrix factorization (PMF) to develop a weighted PMF model for recommendation. The second model, Mixed Membership Bayesian Affinity Estimation (MMBAE), is based on the paradigm of Simultaneous Decomposition and Prediction (SDaP). This model simultaneously learns mixed membership cluster assignments for users and items along with a predictive model for rating prediction within each co-cluster. Experimental evaluation on benchmark datasets are provided for these two models.en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.subjectSide informationen
dc.subjectRecommendation systemsen
dc.subjectSDaPen
dc.subjectMMBAEen
dc.subjectSurveyen
dc.subjectAffinity estimationen
dc.titleA survey on using side information in recommendation systemsen
dc.description.departmentElectrical and Computer Engineeringen
dc.type.genrethesisen
dc.date.updated2012-08-13T14:27:08Zen
dc.identifier.slug2152/ETD-UT-2012-05-5740en


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