Follow me: Personalized IPTV channel switching guide

Chenguang Yu, Hao Ding, Houwei Cao, Yong Liu, Can Yang

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


Compared with the traditional television services, Internet Protocol TV (IPTV) can provide far more TV channels to end users. However, it may also make users feel confused even painful to find channels of their interests from a large number of them. In this paper, using a large IPTV trace, we analyze user channel-switching behaviors to understand when, why and how they switch channels. Based on user behavior analysis, we develop several base and fusion recommender systems that generate in real-time a short list of channels for users to consider whenever they want to switch channels. Evaluation on the IPTV trace demonstrates that our recommender systems can achieve up to 45 percent hit ratio with only three candidate channels. Our recommender systems only need access to user channel watching sequences, and can be easily adopted by IPTV systems with low data and computation overheads.

Original languageEnglish (US)
Title of host publicationProceedings of the 8th ACM Multimedia Systems Conference, MMSys 2017
PublisherAssociation for Computing Machinery, Inc
Number of pages11
ISBN (Electronic)9781450334891
StatePublished - Jun 20 2017
Event8th ACM Multimedia Systems Conference, MMSys 2017 - Taipei, Taiwan, Province of China
Duration: Jun 20 2017Jun 23 2017

Publication series

NameProceedings of the 8th ACM Multimedia Systems Conference, MMSys 2017


Other8th ACM Multimedia Systems Conference, MMSys 2017
Country/TerritoryTaiwan, Province of China


  • Channel switching
  • Fusion method
  • IPTV
  • Realtime recommendation
  • Recommender system

ASJC Scopus subject areas

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


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