A Clustering Approach for Collaborative Filtering Recommendation Using Social Network Analysis

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.