Computer Science – Social and Information Networks
Scientific paper
2011-09-30
Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, pp. 587-592, 2011
Computer Science
Social and Information Networks
6 pages, Proceedings of the 11th International Conference on Intelligent Systems Design and Applications
Scientific paper
10.1109/ISDA.2011.6121719
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 CFS 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 CFSs. A detailed comparison with related literature is also present.
Ferrara Emilio
Fiumara Giacomo
Meo Pasquale de
Provetti Alessandro
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