Recommender systems are widely used for personalization of information on the web and information retrieval systems. Collaborative Filtering (CF) is the most popular recommen- dation technique. However, Classical CF (CCF) systems use only direct links and common features to model relationships between users. This report presents a Behavioral Network Collaborative Filtering approach (BNCF) that uses navi- gational patterns to model relationships between users and exploits social networks techniques, such as prediction link methods, to explore additional links throughout the behav- ioral network. The final aim consists in involving these new links in prediction generation, to improve recommendations quality. The proposed approach is evaluated in terms of ac- curacy on a real usage dataset. The experimentation shows the benefit of exploiting new links to compute predictions in terms of HMAE. Besides, the evaluation of a combined model (that exploits both best accurate BNCF and CCF based models) shows the importance of combining similari- ties of two different networks and its impact on accuracy of high predictions.