A hybrid recommendation algorithm based on social networks

In the light of the problem of collaborative recommendation and content-based cold start, this paper proposes a hybrid recommendation system based on social network. The method is based on the user social relations network. According to the social behavior of user, by establishing the model of social network users, it puts forward the user similarity measure. Then it takes random walk algorithm as a basis and selects out N users who have the highest similarity with the users' interest. The test results show that this method can obtain better recommendation effect and customer satisfaction.

[1]  Le Wu,et al.  Leveraging tagging for neighborhood-aware probabilistic matrix factorization , 2012, CIKM.

[2]  Fernando Ortega,et al.  Generalization of recommender systems: Collaborative filtering extended to groups of users and restricted to groups of items , 2012, Expert Syst. Appl..

[3]  Jennifer Golbeck,et al.  Investigating interactions of trust and interest similarity , 2007, Decis. Support Syst..

[4]  Hui Xiong,et al.  Enhancing Collaborative Filtering by User Interest Expansion via Personalized Ranking , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[5]  Liu Qi,et al.  Recommendations Based on Collaborative Filtering by Exploiting Sequential Behaviors , 2013 .

[6]  Wanlei Zhou,et al.  Learning User Preference Patterns for Top-N Recommendations , 2012, 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology.

[7]  Xin Xia,et al.  A collaborative filtering recommendation algorithm based on contents' genome , 2012 .

[8]  Lei Zhang,et al.  Multi-relational Topic Model for Social Recommendation , 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

[9]  Douglas B. Terry,et al.  Using collaborative filtering to weave an information tapestry , 1992, CACM.

[10]  Mingdong Tang,et al.  Location-Aware Collaborative Filtering for QoS-Based Service Recommendation , 2012, 2012 IEEE 19th International Conference on Web Services.

[11]  Jun Ma,et al.  Learning to recommend with social relation ensemble , 2012, CIKM '12.

[12]  Huang Chuang.  Uncertain Neighbors' Collaborative Filtering Recommendation Algorithm , 2010 .

[13]  GoldbergDavid,et al.  Using collaborative filtering to weave an information tapestry , 1992 .