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Type: Journal article
Title: Interpreting video recommendation mechanisms by mining view count traces
Author: Zhou, Y.
Wu, J.
Chan, T.H.
Ho, S.W.
Chiu, D.M.
Wu, D.
Citation: IEEE Transactions on Multimedia, 2018; 20(8):2153-2165
Publisher: IEEE
Issue Date: 2018
ISSN: 1520-9210
Statement of
Yipeng Zhou, Jiqiang Wu, Terence H. Chan, Siu-Wai Ho, Dah-Ming Chiu and Di Wu
Abstract: 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.
Keywords: Video information diffusion; word-of-mouth recommendation; direct recommendation
Rights: © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
DOI: 10.1109/TMM.2017.2781364
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Appears in Collections:Aurora harvest 8
Electrical and Electronic Engineering publications

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