Social-aware Matrix Factorization for Recommender Systems


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WEIDELE, Daniel, 2013. Social-aware Matrix Factorization for Recommender Systems

@mastersthesis{Weidele2013Socia-29251, title={Social-aware Matrix Factorization for Recommender Systems}, year={2013}, address={Konstanz}, school={Universität Konstanz}, author={Weidele, Daniel} }

<rdf:RDF xmlns:rdf="" xmlns:bibo="" xmlns:dc="" xmlns:dcterms="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dcterms:abstract xml:lang="eng">We review and categorize early approaches of collaborative filtering, before moving towards social-aware matrix factorization models for rating prediction, which we will theoretically compare to each other and to the state of the art model SVD++. We derive a generic social-aware factorization model and show how to improve runtime complexities of social-aware matrix factorization models in general. Moreover we discuss various trust metrics to exploit social network information and propose the application of PageRank as a new alternative in this context. Finally we provide a practical evaluation of presented approaches.</dcterms:abstract> <dc:language>eng</dc:language> <dcterms:issued>2013</dcterms:issued> <dcterms:available rdf:datatype="">2014-11-14T09:33:54Z</dcterms:available> <dc:creator>Weidele, Daniel</dc:creator> <bibo:uri rdf:resource=""/> <dc:date rdf:datatype="">2014-11-14T09:33:54Z</dc:date> <dcterms:rights rdf:resource=""/> <dc:contributor>Weidele, Daniel</dc:contributor> <dcterms:title>Social-aware Matrix Factorization for Recommender Systems</dcterms:title> </rdf:Description> </rdf:RDF>

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