Journal of Universal Computer Science
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 their 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 paper, the authors propose a clustering approach based on the social information of users to derive the recommendations.