JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (3): 51-59.doi: 10.6040/j.issn.1671-9352.0.2016.030

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Mass diffusion on coupled social networks

DENG Xiao-fang1,2, ZHONG Yuan-sheng1*, L(¨overU)Lin-yuan3, WANG Ming-wen4, XIONG Nai-xue1   

  1. 1. College of Information Technology, Jiangxi University of Finance and Economics, Nanchang 330013, Jiangxi, China;
    2. School of Software, Jiangxi Normal University, Nanchang 330022, Jiangxi, China;
    3. Alibaba Research Center for Complexity Sciences, Hangzhou Normal University, Hangzhou 310036, Zhejiang, China;
    4. College of Computer Science, Jiangxi Normal University, Nanchang 330022, Jiangxi, China
  • Received:2016-10-11 Online:2017-03-20 Published:2017-03-20

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Abstract: In the Internet information recommendation application, combining the users social information into recommend systems may further enhance the recommendation accuracy. We transform the social network and the user-commodity bipartite network into a coupled network by considering users as hub nodes and then propose a recommendation algorithm based on the process of mass diffusion dynamics, which integrates the information of friends in the social network and the information of the users selection of items in the user-item bipartite network. It can easily be seen that our approach is an extension of the classical mass diffusion algorithm. Experiments on the real datasets, Friendfeed and Epinions show that the recommendation accuracy of small degree users is improved by 38.48% and 9.17% respectively by comparing our proposed method with the classical mass diffusion algorithm. When the proportion of probe set is 80%, the improvement on recommendation accuracy is 59.05% and 21.62% than that of the classical material diffusion algorithm for all target users. Therefore, the addition of social network information can significantly improve the recommendation accuracy for small degree users.

Key words: mass diffusion, social networks, recommender systems

CLC Number: 

  • TP391
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