Difference between revisions of "2018 AFieldStudyofRelatedVideoRecomm"

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* 2. Shumeet Baluja, Rohan Seth, D. Sivakumar, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, and Mohamed Aly. 2008. Video Suggestion and Discovery for Youtube: Taking Random Walks Through the View Graph. In <i>Proceedings of the 17th International Conference on World Wide Web (WWW '08).</i> ACM, New York, NY, USA, 895--904. [http://doi.acm.org/10.1145/1367497.1367618 doi:10.1145/1367497.1367618]
 
* 2. Shumeet Baluja, Rohan Seth, D. Sivakumar, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, and Mohamed Aly. 2008. Video Suggestion and Discovery for Youtube: Taking Random Walks Through the View Graph. In <i>Proceedings of the 17th International Conference on World Wide Web (WWW '08).</i> ACM, New York, NY, USA, 895--904. [http://doi.acm.org/10.1145/1367497.1367618 doi:10.1145/1367497.1367618]
 
* 3. Michael Bendersky, Lluis Garcia-Pueyo, Jeremiah Harmsen, Vanja Josifovski, and Dima Lepikhin. 2014. Up Next: Retrieval Methods for Large Scale Related Video Suggestion. In <i>Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14).</i> ACM, New York, NY, USA, 1769--1778. [http://doi.acm.org/10.1145/2623330.2623344 doi:10.1145/2623330.2623344]
 
* 3. Michael Bendersky, Lluis Garcia-Pueyo, Jeremiah Harmsen, Vanja Josifovski, and Dima Lepikhin. 2014. Up Next: Retrieval Methods for Large Scale Related Video Suggestion. In <i>Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14).</i> ACM, New York, NY, USA, 1769--1778. [http://doi.acm.org/10.1145/2623330.2623344 doi:10.1145/2623330.2623344]
* 4. Paul Covington, Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In <i>Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16).</i> ACM, New York, NY, USA, 191--198. [http://doi.acm.org/10.1145/2959100.2959190 doi:10.1145/2959100.2959190]
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* 4. [[Paul Covington]], Jay Adams, and Emre Sargin. 2016. Deep Neural Networks for YouTube Recommendations. In <i>Proceedings of the 10th ACM Conference on Recommender Systems (RecSys '16).</i> ACM, New York, NY, USA, 191--198. [http://doi.acm.org/10.1145/2959100.2959190 doi:10.1145/2959100.2959190]
 
* 5. James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 2010. The YouTube Video Recommendation System. In <i>Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys '10).</i> ACM, New York, NY, USA, 293--296. [http://doi.acm.org/10.1145/1864708.1864770 doi:10.1145/1864708.1864770]
 
* 5. James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 2010. The YouTube Video Recommendation System. In <i>Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys '10).</i> ACM, New York, NY, USA, 293--296. [http://doi.acm.org/10.1145/1864708.1864770 doi:10.1145/1864708.1864770]
 
* 6. Josh Dzieza. 2015. The Star Wars History of Trailers. Https://www.theverge.com/2015/12/10/9882404/star-wars-trailers-movie-marketing-youtube-disney
 
* 6. Josh Dzieza. 2015. The Star Wars History of Trailers. Https://www.theverge.com/2015/12/10/9882404/star-wars-trailers-movie-marketing-youtube-disney

Latest revision as of 22:37, 26 March 2020

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Quotes

Abstract

Many video sites recommend videos related to the one a user is watching. These recommendations have been shown to influence what users end up exploring and are an important part of a recommender system. Plenty of methods have been proposed to recommend related videos, but there has been relatively little work that compares competing strategies. We describe a field study of related video recommendations, where we deploy algorithms to recommend related movie trailers. Our results show that recency - and similarity-based algorithms yield the highest click-through rates, and that the recency-based algorithm leads to the most trailer-level engagement. Our findings suggest the potential to design non-personalized yet effective related item recommendation strategies.

References

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  • 2. Shumeet Baluja, Rohan Seth, D. Sivakumar, Yushi Jing, Jay Yagnik, Shankar Kumar, Deepak Ravichandran, and Mohamed Aly. 2008. Video Suggestion and Discovery for Youtube: Taking Random Walks Through the View Graph. In Proceedings of the 17th International Conference on World Wide Web (WWW '08). ACM, New York, NY, USA, 895--904. doi:10.1145/1367497.1367618
  • 3. Michael Bendersky, Lluis Garcia-Pueyo, Jeremiah Harmsen, Vanja Josifovski, and Dima Lepikhin. 2014. Up Next: Retrieval Methods for Large Scale Related Video Suggestion. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '14). ACM, New York, NY, USA, 1769--1778. doi:10.1145/2623330.2623344
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  • 5. James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, and Dasarathi Sampath. 2010. The YouTube Video Recommendation System. In Proceedings of the Fourth ACM Conference on Recommender Systems (RecSys '10). ACM, New York, NY, USA, 293--296. doi:10.1145/1864708.1864770
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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2018 AFieldStudyofRelatedVideoRecommYifan Zhong
Tahir Lazaro Sousa Menezes
Vikas Kumar
Qian Zhao
F. Maxwell Harper
A Field Study of Related Video Recommendations: Newest, Most Similar, Or Most Relevant?10.1145/3240323.32403952018
AuthorYifan Zhong +, Tahir Lazaro Sousa Menezes +, Vikas Kumar +, Qian Zhao + and F. Maxwell Harper +
doi10.1145/3240323.3240395 +
titleA Field Study of Related Video Recommendations: Newest, Most Similar, Or Most Relevant? +
year2018 +