1. Institute of Information and Software, Anhui Xinhua University, Hefei Anhui 230088, China 2. College of Computer Science and Technology, University of Science and Technology of China, Hefei Anhui 230027, China
Abstract：Concerning the problems of current social networking friends recommended methods, such as no obvious user interest and poor correlation between the users, a collaborative filtering algorithm was proposed based on common users and similar labels. The common concerned users were extracted as joint project data, and the custom labels were added to reflect the users' interest. Then the semantic similarity of the labels was calculated to expand the sparse matrix and improve the collaborative filtering recommendation. The experimental results show that, compared with the traditional collaborative filtering algorithm with single index, the proposed algorithm can better reflect the users' interest, and has significant improvement in the recommended accuracy and the average accuracy.
2013, Vol. 33 
