A Novel Collaborative Filtering Algorithm Based on Social Network

With the explosive growth of the Internet, recommendation systems have been widely used by users. Collaborative Filtering (CF) is one of the most popular approaches for determining recommendations. With the development of Social Network Service(SNS), users will be influenced by his or her friends in the social network during the recommendation process. Accordingly considering the relationships(such as friendship, working relationship, kinship and so on) in Recommendation Algorithm(RA) is an important issue. Very little research, however, has focused on this issue. In this work, a Collaborative Filtering algorithm based on Social Network (SNS-CF) was proposed to filter and recommend items. The SNS-CF includes a Star sub-graph structure based recognition algorithm to cluster communities. This can ease data sparse. In another aspect, a Userrank method is used to calculate user's influence degree. This makes important user have greater weight in the recommendation. The experiment results demonstrate that the SNS-CF can improve the recommend precision.