Interpreting video recommendation mechanisms by mining view count traces

Yipeng Zhou, Jiqiang Wu, Terence H. Chan, Siu wai Ho, Dah-Ming Chiu, Di Wu

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)


All large scale online video systems, for example, Netflix and Youku, make a significant investment on video recommendations that can dramatically affect video information diffusion processes among users. However, there is a lack of efficient methodology to interpret how various recommendation mechanisms affect information diffusion processes resulting in the difficulty to evaluate video recommendation efficiency. In this paper, we propose to quantify and explain video recommendation mechanisms by using epidemic models to mine video view count traces. It is well known that an epidemic model is an efficient approach to model information diffusion processes; while view count traces can be viewed as the results of video information diffusion driven by video recommendations. Thus, we propose a framework based on extended epidemic models to quantify and interpret two recommendation mechanisms, that is, direct and word-of-mouth (WOM) recommendations, by fitting video view count traces collected from Tencent Video, a large scale online video system in China. Our approach is a novel methodology to evaluate video recommendation mechanisms, and a new perspective to interpret how recommendation mechanisms drive view count evolution.
Original languageEnglish
Pages (from-to)2153-2165
Number of pages13
JournalIEEE Transactions on Multimedia
Issue number8
Early online date8 Dec 2017
Publication statusPublished - Aug 2018
Externally publishedYes


  • direct recommendation
  • Video information diffusion
  • word-of-mouth recommendation

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