Dynamic User Attribute Discovery on Social Media

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9931)

Abstract

Social media service defines a new paradigm of people communicating, self-expressing and sharing on the Web. Users in today’s social media platforms often post contents, inferring their interests/attributes, which are significant for many Web services such as social recommendation, personalized searching and online advertising. User attributes are temporally dynamic along with internal interest changing and external influence. Based on topic modeling, we present a probabilistic method for dynamic user attribute discovery. Our method automatically detects user attributes and models the dynamics using time windows and decay function, thereby facilitating more accurate recommendation. Evaluation on a Sina Weibo dataset shows the superiority in terms of precision, recall and F-measure as compared to baselines, such as static user attribute modeling.

Keywords

Dynamic user attribute Topic model Time window 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Xiu Huang
    • 1
  • Yang Yang
    • 1
  • Yue Hu
    • 1
  • Fumin Shen
    • 1
  • Jie Shao
    • 1
  1. 1.School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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