Identifying the products to suggest to the target customer in order to best fit his profile of interests is a challenging task in the Social Commerce domain. In this paper we investigate the application of different model-based Collaborative Filtering (CF) techniques and we propose a framework able to provide different suggestions to the customers. The framework is aimed at expanding the customer suggestions in order to guarantee a serendipitous discovery of wished products and at the same time, it is aimed at assisting merchants in discovering potential interests of their customers. We compare several approaches by means of Epinions, MovieLens and Poste Italiane dataset (with real customers). Experimental results show an increased value of coverage of the recommendations provided by our approach without affecting recommendation quality. Moreover, personalized strategy guarantees a high level recommendation for different user navigation behaviors.