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A clustering approach for collaborative filtering recommendation using social network analysis

: Pham, M.C.; Cao, Y.; Klamma, R.; Jarke, M.

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Journal of universal computer science : JUCS 17 (2011), No.4, pp.583-604
ISSN: 0948-695X
ISSN: 0948-6968
Journal Article, Electronic Publication
Fraunhofer FIT ()

Collaborative Filtering(CF) is a well-known technique in recommender systems. CF exploits relationships between users and recommends items to the active user according to the ratings of his/her neighbors. CF suffers from the data sparsity problem, where users only rate a small set of items. That makes the computation of similarity between users imprecise and consequently reduces the accuracy of CF algorithms. In this article, we propose a clustering approach based on the social information of users to derive the recommendations. We study the application of this approach in two application scenarios: academic venue recommendation based on collaboration information and trust-based recommendation. Using the data from DBLP digital library and Epinion, the evaluation shows that our clustering technique based CF performs better than traditional CF algorithms.