WANG Peng, ZHANG Peng, ZHOU Chuan, GUO Li, FANG Binxing, YANG Tao. Modeling Multi-factor Sequential User Behavior Data over Social Networks[J]. Chinese Journal of Electronics, 2016, 25(2): 364-371. doi: 10.1049/cje.2016.03.025
Citation: WANG Peng, ZHANG Peng, ZHOU Chuan, GUO Li, FANG Binxing, YANG Tao. Modeling Multi-factor Sequential User Behavior Data over Social Networks[J]. Chinese Journal of Electronics, 2016, 25(2): 364-371. doi: 10.1049/cje.2016.03.025

Modeling Multi-factor Sequential User Behavior Data over Social Networks

doi: 10.1049/cje.2016.03.025
Funds:  This work is supported by the NSFC (No.61370025, No.61502479), 863 projects (No.2011AA01A103), National Basic Program of China (973 project) (No.2013CB329605), and the Strategic Leading Science and Technology Projects of Chinese Academy of Sciences (No.XDA06030200).
  • Received Date: 2014-03-12
  • Rev Recd Date: 2014-06-26
  • Publish Date: 2016-03-10
  • Modeling dynamic user behavior over online social networks not only helps us understand user behavior patterns on social networks, but also improves the performance of behavior analysis tasks. Time-varying user behavior is commonly influenced by multiple factors:user habit, social influence and external events. Existing works either consider only a part of these factors, or fail to model the dynamics behind user behavior. Thus, they cannot precisely model the user behavior. We present a generative Bayesianmodel HES to model dynamic user behavior data. We take the influential factors and user's selection process as separate latent variables, based on which we can recover the evolving patterns underneath user behavior data sequences. Empirical results on large-scale social networks show that the proposed approach outperforms existing user behavior prediction models by at least 8% w.r.t. prediction accuracy. Our work also unveils some interesting insights underneath social behavior data.
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