Collaborative filtering recommendation algorithms research based on influence and complex network

Recommender systems provide personalized recommendations for products, information or services in electronic commerce. Collaborative filtering is one of the most successful techniques that attempts to recommend items (such as music, movies, web sites) which are likely to be interested to some people. In this paper we study collaborative filtering algorithms from the influences of users and the topology of network. We analyze relationships between users from their scores on same items and how influential users impact others. Whether an item is recommended to a user, we not choose nearest neighbors from all users, but choose them from the influential users for the user, so the neighbors are nearest and strongest influential than others for the user, making recommendation results are more effective and efficiency, which is the principle of the improved algorithm. Relationships between users construct a complex network, so attributes and properties of complex network are also combined with the algorithm of collaborative filtering. Numerical and contrast experiments are proposed to explain and prove the feasibility and efficiency of the recommendation algorithm of collaborative filtering based on influence and complex network. The experimental results show that efficiency of the improved algorithm is better than traditional collaborative filtering algorithm.