Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4947721 | Neurocomputing | 2017 | 29 Pages |
Abstract
User analysis is an important part of social network analysis. Most existing studies model users separately using either user-generated contents or social links among users. In this paper we propose to model users on the Content Curation Social Network (CCSN) in a unified framework by mining user-generated contents as well as social links. We propose a latent Bayesian model Multi-level LDA (MLLDA) that represents users with latent user interests discovered from user-contributed textual description and social links formed by information sharing. We demonstrate that MLLDA can produce accurate user models for community discovery and recommendation on the CCSN.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Lifang Wu, Dan Wang, Xiuzhen Zhang, Shuang Liu, Lei Zhang, Chang Wen Chen,