Collaborative filtering based on dynamic community detection
Sabrine Ben Abdrabbah, Raouia Ayachi, Nahla Ben Amor · 2014
Abstract. With the increase of time-stamped data, the task of recom-mender systems becomes not only to fulfill users interests but also to model the dynamic behavior of their tastes. This paper proposes a novel architecture, called Dynamic Community-based Collaborative filtering (D2CF), that combines both recommendation and dynamic community detection techniques in order to exploit the temporal aspect of the commu-nity structure in real-world networks and to enhance the existing community-based recommendation. The eciency of the proposed D2CF is dealt with a comparative study with a recommendation system based on static com-munity detection and item-based collaborative filtering. Experimental re-sults show a considerable improvement of D2CF recommendation accu-racy, whilst it addresses both of scalability and sparsity problems.