Modeling Users’ Preferences and Social Links in Social Networking Services: A Joint-Evolving Perspective

Researchers have long converged that the evolution of a Social Networking Service (SNS) platform is driven by the interplay between users’ preferences (reflected in user-item consumption behavior) and the social network structure (re- flected in user-user interaction behavior), with both kinds of users’ behaviors change from time to time. However, traditional approaches either modeled these two kinds of behaviors in an isolated way or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of users’ historical preferences and the dynamic social network structure affect the evolution of SNSs. Furthermore, can jointly modeling users’ temporal behaviors in SNSs benefit both behavior prediction tasks? In this paper, we leverage the underlying social theories (i. e. , social influence and the homophily effect) to investigate the interplay and evolution of SNSs. We propose a probabilistic approach to fuse these social theories for jointly modeling users’ temporal behaviors in SNSs. Thus our proposed model has both the explanatory ability and predictive power. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.

Authors

• Le Wu Hefei University of Technology Hefei Comprehensive National Science Center
• Yong Ge
• Qi Liu
• Enhong Chen School of Computer Science and Technology, University of Science and Technology of China, Hefei, China State Key Laboratory of Cognitive Intelligence, Hefei, China
• Bai Long
• Zhenya Huang State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China Institute of Artificial Intelligence, Hefei Comprehensive National Science Center

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