A Friend Recommendation System Using Users' Information of Total Attributes

Social network services, such as Facebook and Twitter in U.S.A., RenRen, QQ and Weibo in China, have grown substantially in recent years. Friend recommendation is an important emerging social network service component, which expands the networks by actively recommending new potential friends to users. We introduce a new friend recommendation system using a user's information of total attributes and based on the Law of total probability. The proposed method can be easily extended according to the number of user's attributes in different social networks. Our experimental results have demonstrated that superior performance the proposed method. In our empirical studies, we have observed that the performance of our algorithm is related with the number of user's friends. Our findings have important and practical applications in social network design and performance.