Friend Recommendation Based on the Similarity of Micro-blog User Model

Online social network has obtained a significant increase in recent years. Making friends is an ordinary way of establishing social relationships with others in online social network. Therefore friend recommendation is becoming a very important aspect and attracting extensive attention in visual communities and social network. Among various online social networks, micro-blog has become increasingly popular. The rapid growth of micro-blog data provides a rich resource for social community mining. In this paper, we present a friend recommendation approach based on the similarity of micro-blog user model. The proposed approach constructs the user model by considering four aspects: user profile, the content information that user posted, the link relationship and the interaction relationship between users. The interaction information such as users´ comments and forwards are considered while calculate the link strength between users. The micro-blog content is divided into different topics which are obtained from an existing topic hierarchy. After we obtained the micro-blog user model, we calculate the interaction based similarity and content based similarity separately. After that, we mix the two kind of similarity together to calculate the similarity of micro-blog user model. At last, we recommend friend to users by this similarity. Experiments show that the friend recommendation approach based on the similarity of micro-blog user model has a higher precision than traditional approaches.