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dc.contributor.advisorShang, Yi, 1967-eng
dc.contributor.authorAlmosallam, Ibrahim Ahmadeng
dc.date.issued2008eng
dc.date.submitted2008 Springeng
dc.descriptionThe entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.eng
dc.descriptionTitle from title screen of research.pdf file (viewed on August 22, 2008)eng
dc.descriptionIncludes bibliographical references.eng
dc.descriptionThesis (M.S.) University of Missouri-Columbia 2008.eng
dc.descriptionDissertations, Academic -- University of Missouri--Columbia -- Computer science.eng
dc.description.abstractCollaborative filtering is one of the most successful techniques for recommendation systems and has been used in many commercial services provided by major companies including Amazon, TiVo and Netflix. In this project we focus on memory-based collaborative filtering (CF). Existing CF techniques work well on dense data but poorly on sparse data. To address this weakness, we propose to use z-scores instead of explicit ratings and introduce a mechanism that adaptively combines global statistics with item-based values based on data density level. A new adaptive framework that encapsulates various CF algorithms and the relationships among them is presented. An adaptive CF predictor is developed that can self adapt from user-based to item-based to hybrid methods based on the amount of available ratings. The experimental results show that the new predictor consistently obtained more accurate predictions than existing CF methods, with the most significant improvement on sparse data sets. When applied to the Netflix Challenge data set, the method performed better than existing CF and singular value decomposition (SVD) methods and achieved 4.67% improvement over Netflix's system.eng
dc.identifier.merlinb64579578eng
dc.identifier.oclc244299859eng
dc.identifier.urihttps://hdl.handle.net/10355/5630
dc.identifier.urihttps://doi.org/10.32469/10355/5630eng
dc.languageEnglisheng
dc.publisherUniversity of Missouri--Columbiaeng
dc.relation.ispartofcollectionUniversity of Missouri--Columbia. Graduate School. Theses and Dissertationseng
dc.subject.lcshComputer algorithmseng
dc.subject.lcshData miningeng
dc.subject.lcshInformation filtering systemseng
dc.titleA new adaptive framework for collaborative filtering predictioneng
dc.typeThesiseng
thesis.degree.disciplineComputer science (MU)eng
thesis.degree.grantorUniversity of Missouri--Columbiaeng
thesis.degree.levelMasterseng
thesis.degree.nameM.S.eng


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