Latent feature learning in social media network

Zhaoquan Yuan, Jitao Sang, Yan Liu, Changsheng Xu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

23 Citations (Scopus)

Abstract

The current trend in social media analysis and application is to use the pre-defined features and devoted to the later model development modules to meet the end tasks. In this work, we claim that representation is critical to the end tasks and contributes much to the model development module. We provide evidence that specially learned feature well addresses the diverse, heterogeneous and collective characteristics of social media data. Therefore, we propose to transfer the focus from the model development to latent feature learning, and present a general feature learning framework based on the popular deep architecture. In particular, following the proposed framework, we design a novel relational generative deep learning model to test the idea on link analysis tasks in the social media networks. We show that the derived latent features well embed both the media content and their observed links, leading to improvement in social media tasks of user recommendation and social image annotation.
Original languageEnglish
Title of host publicationMM 2013 - Proceedings of the 2013 ACM Multimedia Conference
Pages253-262
Number of pages10
DOIs
Publication statusPublished - 18 Nov 2013
Event21st ACM International Conference on Multimedia, MM 2013 - Barcelona, Spain
Duration: 21 Oct 201325 Oct 2013

Conference

Conference21st ACM International Conference on Multimedia, MM 2013
Country/TerritorySpain
CityBarcelona
Period21/10/1325/10/13

Keywords

  • Deep learning
  • Feature learning
  • Link analysis
  • Social media

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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