LAUSR

Traditional collaborative filtering methods always utilize Cosine and Pearson methods to calculate the similarity of users. When the nearest neighbor doesn’t comment the predicted item, then the nearest neighbor has no influence on results, thus affecting the accuracy of collaborative filtering recommendation. And the traditional recommendation systems always have the problems of data sparsity, cold start and so on. In this paper, we consider social relationship and trust relationship, and put forward a novel application recommendation method that combines users’ social relationship and trust relationship. Specifically, we combine social relationship and user preference towards applications to calculate similarity score, we fuse the trust relationship based on familiarity and user reputation to calculate trust score. The final prediction score is calculated by fusing similar relationship and trust relationship properly. And the proposed method can effectively improve accuracy of recommendations.