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Personal Credit Profiling via Latent User Behavior Dimensions on Social Media

  • Guangming Guo
  • Feida Zhu
  • Enhong ChenEmail author
  • Le Wu
  • Qi Liu
  • Yingling Liu
  • Minghui Qiu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9652)

Abstract

Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackling the heterogeneous social data. Moreover, social data only contains extremely weak signals about users’ credit label. To that end, we put forward a Latent User Behavior Dimension based Credit Model (LUBD-CM) to capture these small signals for personal credit profiling. LUBD-CM learns users’ hidden behavior habits and topic distributions simultaneously, and represents each user at a much finer granularity. Specifically, we take a real-world Sina Weibo dataset as the testbed for personal credit profiling evaluation. Experiments conducted on the dataset demonstrate the effectiveness of our approach: (1) User credit label can be predicted using LUBD-CM with a considerable performance improvement over state-of-the-art baselines; (2) The latent behavior dimensions have very good interpretability in personal credit profiling.

Keywords

Credit Risk User Attribute Behavior Data Social Data User Generate Content 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgement

This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the National High Technology Research and Development Program of China (Grant No. 2014AA015203), the Science and Technology Program for Public Wellbeing (Grant No. 2013GS340302) and the CCF-Tencent Open Research Fund. This work was also partially supported by the Pinnacle Lab for Analytics @ Singapore Management University.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Guangming Guo
    • 1
    • 2
  • Feida Zhu
    • 2
  • Enhong Chen
    • 1
    Email author
  • Le Wu
    • 1
  • Qi Liu
    • 1
  • Yingling Liu
    • 1
  • Minghui Qiu
    • 2
  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.School of Information SystemsSingapore Management UniversitySingaporeSingapore

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