creators_name: De Meo, Pasquale creators_name: Ferrara, Emilio creators_name: Fiumara, Giacomo creators_name: Provetti, Alessandro type: confpaper datestamp: 2011-10-01 00:34:40 lastmod: 2011-10-01 00:34:40 metadata_visibility: show title: Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships ispublished: pub subjects: comp-sci-art-intel full_text_status: public abstract: Matrix Factorization techniques have been successfully applied to raise the quality of suggestions generated by Collaborative Filtering Systems (CFSs). Traditional CFSs based on Matrix Factorization operate on the ratings provided by users and have been recently extended to incorporate demographic aspects such as age and gender. In this paper we propose to merge CF techniques based on Matrix Factorization and information regarding social friendships in order to provide users with more accurate suggestions and rankings on items of their interest. The proposed approach has been evaluated on a real-life online social network; the experimental results show an improvement against existing CF approaches. A detailed comparison with related literature is also present date: 2011 date_type: published refereed: TRUE citation: De Meo, Pasquale and Ferrara, Emilio and Fiumara, Giacomo and Provetti, Alessandro (2011) Improving Recommendation Quality by Merging Collaborative Filtering and Social Relationships. [Conference Paper] document_url: http://cogprints.org/7651/1/isda2011-rec-sys.pdf