The exponential evolution of information on theWeb and information retrieval systems engendered a heightened need for content personalization. Rec- ommender systems are widely used for this purpose. Collaborative Filtering (CF) is the most popular recommendation technique. However, CF systems are very dependent on the availability of ratings to model relationships between users and generate accurate predictions. Thus, no recommendation can be computed for new incorporated items. This paper proposes an original way to alleviate latency problem by harnessing behavioral leaders in the context of a behavioral network. In this network, users are linked when they have similar navigational behaviors. We present an algorithm that aims at detecting behavioral leaders based on their connectivity and their potentiality of prediction. These leaders represent the en- try nodes that the recommender system targets so as to predict the preferences of their neighbors about new items. This approach is evaluated in terms of preci- sion using a real usage dataset. The results of the experimentation show that our approach not only solves the latency problem, it also leads to a precision higher than standard CF.