Modeling Multi‐factor Sequential User Behavior Data over Social Networks. Issue 2 (1st March 2016)

• Record Type: Journal Article
• Authors: Wang, Peng; Zhang, Peng; Zhou, Chuan; Guo, Li; Fang, Binxing; Yang, Tao
• Is Part Of: Chinese journal of electronics. Volume 25:Issue 2(2016)
• Page Start: 364
• Page End: 371
• Publication Date: 2016-03-01
• Subjects: Malicious user detection -- User behavior -- Social networks -- Bayesian model -- Social influence; Bayes methods -- social networking (online); social behavior data -- user behavior prediction models -- user behavior data sequences -- user selection process -- generative Bayesian model HES -- external events -- social influence -- user habit -- time‐varying user behavior -- behavior analysis tasks -- dynamic user behavior modeling -- social networks -- multifactor sequential user behavior modelling; Electronics -- Periodicals; Electronics -- China -- Periodicals; Electronics; China; Periodicals; 621.38105
• DOI: 10.1049/cje.2016.03.025
• Languages: English
• ISSNs: 1022-4653

Abstract

Abstract : 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 Bayesian model 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.