NDLI: Modeling Multi-factor Sequential User Behavior Data over Social Networks

Content Provider	IET Digital Library
Author	Wang, Peng Zhang, Peng Zhou, Chuan Guo, Li Fang, Binxing Yang, Tao
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.
Starting Page	364
Ending Page	371
Page Count	8
ISSN	10224653
Volume Number	25
e-ISSN	20755597
Issue Number	Issue 2, Mar (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/25/2
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2016.03.025
Journal	Chinese Journal of Electronics
Publisher Date	2016-03-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Bayes Method Behavior Analysis Tasks Dynamic User Behavior Modeling External Events Generative Bayesian Model HES Multifactor Sequential User Behavior Modelling Social And Behavioural Sciences Computing Social Behavior Data Social Influence Social Network Social Networking (online) Statistics Time-varying User Behavior User Behavior Data Sequence User Behavior Prediction Model User Habit User Selection Process
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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