Modeling the Evolution of Users' Preferences and Social Links in Social Networking Services

Le Wu, Yong Ge, Qi Liu, Enhong Chen, Richang Hong, Junping Du, Meng Wang

Research output: Contribution to journalArticlepeer-review

76 Scopus citations

Abstract

Sociologists have long converged that the evolution of a Social Networking Service(SNS) is driven by the interplay between users' preferences (reflected in user-item interaction behavior) and the social network structure (reflected in user-user interaction behavior). Nevertheless, traditional approaches either modeled these two kinds of behaviors in isolation or relied on a static assumption of a SNS. Thus, it is still unclear how do the roles of the dynamic social network structure and users' historical preferences affect the evolution of SNSs. Furthermore, can transforming the underlying social theories in the platform evolution modeling process benefit both behavior prediction tasks? In this paper, we incorporate the underlying social theories to explain and model the evolution of users' two kinds of behaviors in SNSs. Specifically, we present two kinds of representations for users' behaviors: a direct (latent) representation that presumes users' behaviors are represented directly (latently) by their historical behaviors. Under each representation, we associate each user's two kinds of behaviors with two vectors at each time. Then, for each representation, we propose the corresponding learning model to fuse the interplay between users' two kinds of behaviors. Finally, extensive experimental results demonstrate the effectiveness of our proposed models for both user preference prediction and social link suggestion.

Original languageEnglish (US)
Article number7839995
Pages (from-to)1240-1253
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume29
Issue number6
DOIs
StatePublished - Jun 1 2017
Externally publishedYes

Keywords

  • User modeling
  • link prediction
  • social networking services
  • user interest modeling

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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