Multimedia Tools and Applications

, Volume 78, Issue 2, pp 1999–2015 | Cite as

Personalized channel recommendation on live streaming platforms

  • Chen-Yi LinEmail author
  • Han-Shen Chen


With unceasing technological advancements, an increasing number of viewers are watching channels through live streaming platforms, and live streaming technologies are developing rapidly. However, as thousands of channels are broadcasting on live streaming platforms, it is difficult for viewers to find their favorite channels. As a result, an accurate channel recommendation technique is required for the viewers. The current method of promoting live streaming channels recommends the most popular channels to viewers, but this ignores viewers’ personal preferences. Therefore, we cluster viewers based on their personal preferences so that one cluster of viewers contains the viewers with similar favorite channels. In this way, the channels liked by viewers can be recommended to other viewers in the same group. In addition, our recommendation technique also considers viewers’ loyalty towards a particular channel. In the experiment, a currently popular live streaming gaming platform, Twitch, is used for the analysis. The results confirm that our proposed recommendation technique is more accurate than the existing recommendation techniques.


Recommendation system Live streaming Clustering Personal preference 



We would like to thank the anonymous reviewers for their comments. This work was supported by the Ministry of Science and Technology of Republic of China under grant MOST 105-2221-E-025-011 and MOST 106-2221-E-025-012.

Declarations Authors’ contributions

CYL designed the research, helped with the interpretation and analysis of the experiments, and wrote the manuscript. HSC performed the experiments and wrote the manuscript. All authors read and approved the final manuscript.

Compliance with ethical standards

Competing interests

The authors declare that they have no competing interests.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information ManagementNational Taichung University of Science and TechnologyTaichungTaiwan
  2. 2.Institute of Computer Science and EngineeringNational Chiao Tung UniversityHsinchuTaiwan

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