关键词: 社交网络, 贝叶斯网, 用户相似性, 并行推理, Hadoop

Abstract: Discovering user similarity in social network as a basic research of social media data analysis can be used in product recommendation and social network user relationship evolution effectively. To represent complex correlations and their uncertainties among social network users and improve the accuracy of user similarity in mass social network theoretically, this paper proposes an effective method for discovering user similarity in social network combining network topological structure and dependence between users based on Bayesian network, an important probabilistic graphical model. To improve the scalability of the proposed method and solve the storage and computation problem of mass data, this paper proposes Bayesian network distributed storage and parallel reasoning algorithm based on Hadoop platform. The experimental results verify that the proposed method is effective and correct.

Key words: social network, Bayesian network, user similarity, parallel reasoning, Hadoop