A Study of YouTube recommendation graph based on measurements and stochastic tools

Yonathan Portilla 1, 2 Alexandre Reiffers 1, 2 Eitan Altman 1 Rachid El-Azouzi 2
1 MAESTRO - Models for the performance analysis and the control of networks
CRISAM - Inria Sophia Antipolis - Méditerranée
2 LIA - Laboratoire Informatique d'Avignon
Abstract : — The Youtube recommendation is one the most important view source of a video. In this paper, we focus on the recommendation system in boosting the popularity of videos. We first construct a graph that captures the recommendation system in Youtube and study empirically the relationship between the number of views of a video and the average number of views of the videos in its recommendation list. We then consider a random walker on the recommendation graph, i.e. a random user that browses through videos such that the video it chooses to watch is selected randomly among the videos in the recommendation list of the previous video it watched. We study the stability properties of this random process and we show that the trajectory obtained does not contain cycles if the number of videos in the recommendation list is small (which is the case if the computer's screen is small). Index Terms— Analysis of recommendation system, drift stability analysis, Youtube
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Communication dans un congrès
3rd International Workshop on Big Data and Social Networking Management and Security (BDSN 2015), Dec 2015, Limassol, Cyprus. Proceedings of 3rd International Workshop on Big Data and Social Networking Management and Security (BDSN 2015)
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Yonathan Portilla, Alexandre Reiffers, Eitan Altman, Rachid El-Azouzi. A Study of YouTube recommendation graph based on measurements and stochastic tools. 3rd International Workshop on Big Data and Social Networking Management and Security (BDSN 2015), Dec 2015, Limassol, Cyprus. Proceedings of 3rd International Workshop on Big Data and Social Networking Management and Security (BDSN 2015). 〈hal-01217047〉

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