Recommender systems have been proposed to exploit the potential of social network by filtering the information and offer recommendations to a user that he is predicted to like. Collaborative Filtering (CF) is believed to be a suitable underlying technique for recommender systems on social network, because CF gathers tastes of similar users; and social network provides such a collaborative social environment. One inherent challenge however for running CF on social network is quantitative estimation of trust between friends. Although many researchers investigated trust metrics and models in social network, none so far has efficiently integrated them in a CF algorithm. The contribution of this paper is a framework of collaborative filtering on social network, and a novel approach in measuring trust factors by data-mining over a survey dataset provided by The Facebook Project. The quantitatively estimated trust factors can be used as input parameters in the CF algorithm. Facebook is taken as a case study here to illustrate the concepts.