Connection discovery using shared images by Gaussian relational topic model

Xiaopeng Li, Ming Cheung, James She

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

Social graphs, representing online friendships among users, are one of the fundamental types of data for many applications, such as recommendation, virality prediction and marketing in social media. However, this data may be unavailable due to the privacy concerns of users, or kept private by social network operators, which makes such applications difficult. Inferring users' interests and discovering users' connections through their shared multimedia content has attracted more and more attention in recent years. This paper proposes a Gaussian relational topic model for connection discovery using user shared images in social media. The proposed model not only models users' interests as latent variables through their shared images, but also considers the connections between users as a result of their shared images. It explicitly relates user shared images to user connections in a hierarchical, systematic and supervisory way and provides an end-to-end solution for the problem. This paper also derives efficient variational inference and learning algorithms for the posterior of the latent variables and model parameters. It is demonstrated through experiments with over 200k images from Flickr that the proposed method significantly outperforms the methods in previous works.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
EditorsRonay Ak, George Karypis, Yinglong Xia, Xiaohua Tony Hu, Philip S. Yu, James Joshi, Lyle Ungar, Ling Liu, Aki-Hiro Sato, Toyotaro Suzumura, Sudarsan Rachuri, Rama Govindaraju, Weijia Xu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages931-936
Number of pages6
ISBN (Electronic)9781467390040
DOIs
Publication statusPublished - 2016
Externally publishedYes
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: 5 Dec 20168 Dec 2016

Publication series

NameProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

Conference

Conference4th IEEE International Conference on Big Data, Big Data 2016
Country/TerritoryUnited States
CityWashington
Period5/12/168/12/16

Keywords

  • Bayesian
  • connection
  • discovery
  • recommendation
  • social network analysis
  • topic model
  • user shared images
  • variational inference

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