KEYWORDS: Social networks, Systems modeling, Visualization, Performance modeling, Switches, Lutetium, Process modeling, Data processing, Social network analysis, Pattern recognition
Currently, social tagging systems have been adopted by many social websites. As tags help users to
browse social content effectively, personalized tag prediction problem becomes important in social
networks. In this paper, we present a new generative probabilistic model to solve personalized tag
prediction problem. Differently with previous methods, we consider social influence between users
and friends into this model. We bring two major contributions: 1) We propose a new probabilistic
model which considers in social influence to describe users' actual tagging activities; 2) Based on
this model, we propose a new approach to perform personalized tag prediction task. Experimental
results on a real-world dataset crawled from Last.fm show that our method outperforms other
methods.
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