A weighted-graph based approach for solving the cold start problem in collaborative recommender systems

Collaborative recommender systems are systems which recommend to a user resources may be of his interests based on the opinions of other users who share with him the same center of interest. However, they suffer from the cold start problem of a new user who starts with an empty profile and encounters a difficulty of communication with his community members. In this paper, we propose a new approach to overcome the latter problem by modeling it graphically and by opting for the detection of critical nodes to identify the best representatives that help to adapt a new user in each social community.