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World Wide Web

, Volume 20, Issue 2, pp 417–435 | Cite as

Discerning individual interests and shared interests for social user profiling

  • Enhong ChenEmail author
  • Guangxiang ZengEmail author
  • Ping Luo
  • Hengshu Zhu
  • Jilei Tian
  • Hui Xiong
Article

Abstract

Traditionally, research about social user profiling assumes that users share some similar interests with their followees. However, it lacks the studies on what topic and to what extent their interests are similar. Our study in online sharing sites reveals that besides shared interests between followers and followees, users do maintain some individual interests which differ from their followees. Thus, for better social user profiling we need to discern individual interests (capturing the uniqueness of users) and shared interests (capturing the commonality of neighboring users) of the users in the connected world. To achieve this, we extend the matrix factorization model by incorporating both individual and shared interests, and also learn the multi-faceted similarities unsupervisedly. The proposed method can be applied to many applications, such as rating prediction, item level social influence maximization and so on. Experimental results on real-world datasets show that our work can be applied to improve the performance of social rating. Also, it can reveal some interesting findings, such as who likes the “controversial” items most, and who is the most influential in attracting their followers to rate an item.

Keywords

Social recommendation User profiling Collaborative filtering Information filtering Social and behavioral sciences 

Notes

Acknowledgments

This research was partially supported by grants from the National Science Foundation for Distinguished Young Scholars of China (Grant No. 61325010), the Natural Science Foundation of China (Grant No.s 61403358 and 71329201), the Science and Technology Program for Public Wellbeing (Grant No. 2013GS340302), and the National High Technology Research and Development Program of China (Grant No. 2014AA015203). Ping was supported by the National Natural Science Foundation of China (No.61473274), and National High Technology Research and Development Program of China (No.2014AA015105).

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS)Institute of Computing Technology, CASBeijingChina
  3. 3.Baidu Research-Big Data LaboratoryBeijingChina
  4. 4.BMW TechnologyChicagoUSA
  5. 5.Management Science and Information Systems Department, Rutgers Business SchoolRutgers UniversityNewarkUSA

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