research-article

The impact of YouTube recommendation system on video views

Authors:

Renjie Zhou,

Samamon Khemmarat,

Lixin GaoAuthors Info & Claims

IMC '10: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement

Pages 404 - 410

https://doi.org/10.1145/1879141.1879193

Published: 01 November 2010 Publication History

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Abstract

Hosting a collection of millions of videos, YouTube offers several features to help users discover the videos of their interest. For example, YouTube provides video search, related video recommendation and front page highlight. The understanding of how these features drive video views is useful for creating a strategy to drive video popularity. In this paper, we perform a measurement study on data sets crawled from YouTube and find that the related video recommendation, which recommends the videos that are related to the video a user is watching, is one of the most important view sources of videos. Despite the fact that the YouTube video search is the number one source of views in aggregation, the related video recommendation is the main source of views for the majority of the videos on YouTube. Furthermore, our results reveal that there is a strong correlation between the view count of a video and the average view count of its top referrer videos. This implies that a video has a higher chance to become popular when it is placed on the related video recommendation lists of popular videos. We also find that the click through rate from a video to its related videos is high and the position of a video in a related video list plays a critical role in the click through rate. Finally, our evaluation of the impact of the related video recommendation system on the diversity of video views indicates that the current recommendation system helps to increase the diversity of video views in aggregation.

References

[1]

Google adwords click through rates per position. http://www.accuracast.com/seo-weekly/ adwords-clickthrough.php, October 2009.

Google Scholar

[2]

S. Axon. Youtube to let users charge rental fees. http://www.cnn.com/2010/TECH/05/03/youtube. rental/index.html, May 2010.

Google Scholar

[3]

M. Cha, H. Kwak, P. Rodriguez, Y. Ahn, and S. Moon. I Tube, You Tube, Everybody Tubes: Analyzing the World's Largest User Generated Content Video System. In Proceedings of ACM Internet measurement Conference(IMC), San Diego, CA, USA, October 2007.

Digital Library

Google Scholar

[4]

X. Cheng, C. Dale, and J. Liu. Statistics and social network of youtube videos. In International Workshop on Quality of Service (IWQoS'08), pages 229--238. IEEE, June 2008.

Crossref

Google Scholar

[5]

D. Fleder and K. Hosanagar. Recommender systems and their impact on sales diversity. In Proceedings of the 8th ACM conference on Electronic commerce, page 199. ACM, 2007.

Digital Library

Google Scholar

[6]

D. Fleder and K. Hosanagar. Blockbuster culture's next rise or fall: The impact of recommender systems on sales diversity. Management Science, 55(5):697--712, 2009.

Digital Library

Google Scholar

[7]

P. Gill, Z. Li, M. Arlitt, and A. Mahanti. Characterizing Users Sessions on YouTube. In Proceedings of SPIE/ACM Conference on Multimedia Computing and Networking (MMCN), Santa Clara, USA, January 2008.

Google Scholar

[8]

J. Leskovec, A. Singh, and J. Kleinberg. Patterns of influence in a recommendation network. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD, pages 380--389. Springer-Verlag, 2005.

Digital Library

Google Scholar

[9]

P. White. How many videos are on youtube? http: //www.associatedcontent.com/article/1927414/ how_many_videos_are_on_youtube.html?cat=15, July 2009.

Google Scholar

[10]

B. Zhao, Y. Li, J. Lui, and D. Chiu. Mathematical Modeling of Advertisement and Influence Spread in Social Networks. In NetEcon:Workshop on the Economics of Networks, Systems and Computation, 2009.

Google Scholar

[11]

T. Zhou, Z. Kuscsik, J. Liu, M. Medo, J. Wakeling, and Y. Zhang. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 107(10):4511, 2010.

Crossref

Google Scholar

[12]

M. Zink, K. Suh, Y. Gu, and J. Kurose. Watch Global, Cache Local: YouTube Network Traffic at a Campus Network - Measurements and Implications. In Proceedings of SPIE/ACM Conference on Multimedia Computing and Networking (MMCN), Santa Clara, USA, January 2008.

Crossref

Google Scholar

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Published In

IMC '10: Proceedings of the 10th ACM SIGCOMM conference on Internet measurement

November 2010

496 pages

ISBN:9781450304832

DOI:10.1145/1879141

Program Chair:
Mark Allman
International Computer Science Institute

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 November 2010

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November 1 - 30, 2010

Melbourne, Australia

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Shakespeare DChareyron VRoth C(2025)Reframing the filter bubble through diverse scale effects in online music consumptionScientific Reports10.1038/s41598-024-75967-015:1Online publication date: 3-Feb-2025
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Index Terms

Recommendations

How YouTube videos are discovered and its impact on video views

Boosting video popularity through recommendation systems

Parallel Ratio Based CF for Recommendation System

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