SNS trend-based TV program recommendation scheme

Daeyong Kim, Daehoon Kim, Seungmin Rho, Eenjun Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, as countries have switched over from analog broadcasting to digital broadcasting, various services for digital broadcasting have been introduced to enhance customer satisfaction. However, users still experience difficulties in selecting what they want to watch due to the large volume of TV contents. Often, social trends and TV programs are closely related; TV programs could be produced to deal with some hot issues and public interest could be formed for some TV program. Nowadays, such public interest can be detected as trends from popular SNS such as Twitter. In this paper, we propose a SNS trend-based TV program recommendation scheme. To do that, we first extract trend keywords from Twitter stream data and augment them semantically by referring to recommendation log and portal sites. Finally, we analyze EPGs to find matched TV programs to the trends. We implemented a prototype system to evaluate the effectiveness of our scheme.

Original languageEnglish
Title of host publicationElectronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013 - San Jose, CA, United States
Duration: 2013 Jul 152013 Jul 19

Publication series

NameElectronic Proceedings of the 2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013

Other

Other2013 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2013
CountryUnited States
CitySan Jose, CA
Period13/7/1513/7/19

Keywords

  • EPG
  • SNS analysis
  • TV contents recommendation
  • digital broadcasting
  • trend keyword

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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