SoRS: Social recommendation using global rating reputation and local rating similarity

Recommendation is an important and also challenging problem in online social networks. It needs to consider not only users' personalized interests, but also social relations between users. Indeed, in practice, users are often inclined to accept recommendations from friends or opinion leaders (users with high reputations). In this paper, we present a novel recommendation framework, social recommendation using global rating reputation and local rating similarity, which combine user reputation and social similarity based on ratings. User reputation can be obtained by iteratively calculating the correlation of historical ratings of user and intrinsic qualities of items. We view the user reputation as the user's global influence and the similarity based on rating of social relation as the user's local influence, introduce it in the basic social recommender model. Thus users with high reputation have a strong influence on the others, and on the other hand, the effect of a user with low reputation has been weakened. The recommendation accuracy of proposed framework can be improved by effectively removing nature noise because of less rigorous user ratings and strengthening the effect of user influence with high reputation. We also improve the similarity based on ratings by avoiding the high similarity with the less common ratings between friends. We evaluate our approach on three datasets including Movielens, Epinions and Douban. Empirical results demonstrate that proposed framework achieves significant improvements on recommendation accuracy. User reputation and local similarity which are both based on ratings have a lot of helpful in improvement of prediction accuracy. The reputation also can help to improve the recommendation precision with the small training sets.