The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
The impact of YouTube recommendation system on video views
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.
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Cites
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., & Moon, S. (2007) · Proceedings of the 7th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1298306.1298309 · Cited by 1,576
Solving the apparent diversity-accuracy dilemma of recommender systems
Zhou, T., Kuscsik, Z., Liu, J.-G., Medo, M., Wakeling, J. R., & Zhang, Y.-C. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1000488107 · Cited by 972
Blockbuster Culture's Next Rise or Fall: The Impact of Recommender Systems on Sales Diversity
Fleder, D., & Hosanagar, K. (2009) · Management Science · doi:10.1287/mnsc.1080.0974 · Cited by 745
Statistics and Social Network of YouTube Videos
Cheng, X., Dale, C., & Liu, J. (2008) · 2008 16th Interntional Workshop on Quality of Service · doi:10.1109/iwqos.2008.32 · Cited by 720
Patterns of Influence in a Recommendation Network
Leskovec, J., Singh, A., & Kleinberg, J. (2006) · Lecture Notes in Computer Science · doi:10.1007/11731139_44 · Cited by 307
Recommender systems and their impact on sales diversity
Fleder, D. M., & Hosanagar, K. (2007) · Proceedings of the 8th ACM conference on Electronic commerce · doi:10.1145/1250910.1250939 · Cited by 167
Cited by
Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023) · Computers & Education · doi:10.1016/j.compedu.2022.104684 · Cited by 605
Algorithmic Effects on the Diversity of Consumption on Spotify
Anderson, A., Maystre, L., Anderson, I., Mehrotra, R., & Lalmas, M. (2020) · Proceedings of The Web Conference 2020 · doi:10.1145/3366423.3380281 · Cited by 206
“Fleder and Hosanagar found that in aggregate, diversity decreases as a result of recommender system use in sales [11], whereas Zhou et al. found that the YouTube recommendation system increased the diversity of video views in 2010 [31].”
BackgroundFrom Moral Panic to Systemic Change: Making Child-Centered Design the Default
Radesky, J., & Hiniker, A. (2022) · International Journal of Child-Computer Interaction · doi:10.1016/j.ijcci.2021.100351 · Cited by 44
Generative artificial intelligence–mediated confirmation bias in health information seeking
Lopez‐Lopez, E., Abels, C. M., Holford, D., Herzog, S. M., & Lewandowsky, S. (2025) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.15413 · Cited by 38
Metadata
- Authors
- Zhou, R., Khemmarat, S., & Gao, L.
- Venue
- Proceedings of the 10th ACM SIGCOMM conference on Internet measurement
- Publisher
- ACM
- Published
- 2010-11
- Pages
- 404-410
- OpenAlex
- W1977606842
- Cited by
- 347
- References
- 6
- FWCI
- 11.47
- Type
- conference paper
- Language
- en
- Open access
- closed
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