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Advances in Multimedia
Volume 2011, Article ID 852518, 19 pages
http://dx.doi.org/10.1155/2011/852518
Research Article

From Community Detection to Mentor Selection in Rating-Free Collaborative Filtering

LORIA—Nancy-Université, 615 Rue du Jardin Botanique, 54506 Vandoeuvre-lès-Nancy, France

Received 5 October 2010; Revised 27 December 2010; Accepted 10 January 2011

Academic Editor: Andrea Prati

Copyright © 2011 Armelle Brun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The number of items that users can now access when navigating on the Web is so huge that these might feel lost. Recommender systems are a way to cope with this profusion of data by suggesting items that fit the users needs. One of the most popular techniques for recommender systems is the collaborative filtering approach that relies on the preferences of items expressed by users, usually under the form of ratings. In the absence of ratings, classical collaborative filtering techniques cannot be applied. Fortunately, the behavior of users, such as their consultations, can be collected. In this paper, we present a new approach to perform collaborative filtering when no rating is available but when user consultations are known. We propose to take inspiration from local community detection algorithms to form communities of users and deduce the set of mentors of a given user. We adapt one state-of-the-art algorithm so as to fit the characteristics of collaborative filtering. Experiments conducted show that the precision achieved is higher then the baseline that does not perform any mentor selection. In addition, our model almost offsets the absence of ratings by exploiting a reduced set of mentors.