Discerning individual interests and shared interests for social user profiling

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• 作者：Enhong Chen ; Guangxiang Zeng ; Ping Luo ; Hengshu Zhu ; Jilei Tian…
• 关键词：Social recommendation ; User profiling ; Collaborative filtering ; Information filtering ; Social and behavioral sciences
• 刊名：World Wide Web
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：20
• 期：2
• 页码：417-435
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Information Systems Applications (incl.Internet); Database Management; Operating Systems;
• 出版者：Springer US
• ISSN：1573-1413
• 卷排序：20

文摘

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.