Discerning individual interests and shared interests for social user profiling |
| |
Authors: | Email author" target="_blank">Enhong?ChenEmail author Email author" target="_blank">Guangxiang?ZengEmail author Ping?Luo Hengshu?Zhu Jilei?Tian Hui?Xiong |
| |
Affiliation: | 1.School of Computer Science and Technology,University of Science and Technology of China,Hefei,China;2.Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS),Institute of Computing Technology, CAS,Beijing,China;3.Baidu Research-Big Data Laboratory,Beijing,China;4.BMW Technology,Chicago,USA;5.Management Science and Information Systems Department, Rutgers Business School,Rutgers University,Newark,USA |
| |
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: | |
本文献已被 SpringerLink 等数据库收录! |
|