Research And Implementation Of Recommendation Algorithm Integrated Social Relationship

With the rapid development of Web2.0technology and various social networking sites, the traditional recommendation system has ushered in new opportunities and challenges. In social network users are more likely to accept the recommendation from friends, but traditional recommendation system often neglects the social relationship between users, thus affecting the recommendation results. Therefore, in order to promote the development of recommendation systems and improve accuracy and validity of personalized recommendation results, how to integrate the social relationship into the recommendation systems has become an important issue in social networking services.Based on the research of existing collaborative filtering algorithm and social network on the recommendation system, the following aspects were studied and summarized in this paper.(1) Traditional friends recommendation algorithm in social network considers only the common friends and neglects the trust propagation between users. For this defect, this paper proposes a computing model of indirect trust. Friends will be recommended according to the indirect trust. In addition, experiments show that integrating the user’s interest similarity into friends recommendation in social network can further improve the quality of friends recommendation.(2) On the basis of the existing social recommender systems, the paper proposes a collaborative filtering algorithm based on trust and interest clustering. Firstly, the algorithm takes synthetically into account users’trust degree and the similarity of interest and users are clustered based on the composite similarity. Then we find out the nearest neighbors of target user in some similar clusters and predict the unknown user rates. Finally, experimental results show that the proposed algorithm can obtain more accurate social recommendation as well as improving the real-time performance to some extent.(3) Taking into account characteristics of the user groups, a random walk recommendation algorithm based on users groups mining is proposed with the social network analysis method. The algorithm mines firstly social network users relations and get hidden social network user groups. Then it proposes a new social recommendation model that integrates user groups characteristic by using random walk method. Experiments indicate the algorithm can greatly narrowed the scope of searching users and the effect of recommendation is better than the existing methods of collaborative filtering and trust-based algorithm.