Collaborative Filtering based on Dynamic Community Detection

With the increase of time-stamped data, the task of recommender 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 community structure in real-world networks and to enhance the existing communitybased recommendation. The e ciency of the proposed D2CF is dealt with a comparative study with a recommendation system based on static community detection and item-based collaborative filtering. Experimental results show a considerable improvement of D2CF recommendation accuracy, whilst it addresses both of scalability and sparsity problems.