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  4. A clustering approach for collaborative filtering recommendation using social network analysis
 
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2011
Journal Article
Title

A clustering approach for collaborative filtering recommendation using social network analysis

Abstract
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.
Author(s)
Pham, M.C.
Cao, Y.
Klamma, R.
Jarke, M.
Journal
Journal of universal computer science : JUCS  
DOI
10.3217/jucs-017-04-0583
Language
English
Fraunhofer-Institut für Angewandte Informationstechnik FIT  
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