Recommender systems are widely used for automatic personalization of information on web sites and informa- tion retrieval systems. Collaborative Filtering (CF) is the most popular recommendation technique, but several CF systems still suffer from problems like data rating availability and space dimensionality for neighborhood selection. In this paper, we present a new CF approach (PSN-CF) that uses usage traces to model users. These traces are used to estimate ratings that will be employed to generate clusters. Then, the PSN-CF evaluates navigational correlations between users within these clusters. Predictions are performed in a following step. The performance of PSN-CF is evaluated in terms of accuracy and time processing on a real usage dataset. We show that PSN-CF highly improves the accuracy of predictions in terms of MAE. Moreover, the use of clustering and positive sequences before computing the navigational correlations contributes to an important reduction of time processing.