An Approach of Connectedness of users–items networks and recommender systems based on Collaborative filtering

Recommender systems have become an important issue in network science. Collaborative filtering and its variants are the most widely usedapproaches for building recommender systems, whichhave received great attention in both academia and industry. In this paper, it studied the relationshipbetween recommender systems and connectivity of usersitems bipartite network. Since recommending an item to a user equals to adding a new link to the users-itemsbipartite graph, the intuition behind the proposed approach is that the items should be recommended to theusers such that the least increase in the connectedness ofthe network is obtained. In this method recommended items are selected based on the eigenvector correspondingto the algebraic connectivity of the graph – the second smallest eigenvalue of the Laplacian matrix. The results has obtained, is the higher accuracy and decrease computation complexity

An Approach of Connectedness of users–items networks and recommender systems based on Collaborative filtering Keywords:

complex networks , recommender systems , Algebraic connectivity