A Recommender System for YouTube Video based on deep neural network
DOI:
https://doi.org/10.26438/ijcse/v7i6.160163Keywords:
Boost, Sophisticated, Deep LearningAbstract
YouTube is video sharing sites where a user can create own profile, upload videos and watch the multiple videos. The YouTube uses the recommender system. With the help of recommendation system we boost the popularity of videos. The recommendation is based on the relation between the number of views and the average number of views on particular videos. The recommendation also considers the likes and comment section. When the viewers view the same type of video then YouTube recommends the same type of video. The YouTube recommendation is based on machine learning technique. In machine learning we used the concept of the deep learning method. With the help of deep learning we solve the sophisticated problem. In this paper we see the working of deep neural network to recommend the video based on viewers.
References
[1] M. Deshpande and G. Karypis, ”Item-based top-n recommendation algorithms”, ACM Trans. Inf. Syst., 22(1):143–177, 2004
[2] James Davidson,Benjamin Liebald,Junning Liu,Palash Nandy,Taylor Van Vleet, "The YouTube Video Recommendation System", January 2010.
[3] S. Huffman. Search evaluation at Google.http://googleblog.blogspot.com/2008/09/search-evaluation-at-google.html, 2008.
[4] Shuai Zhang,Lina Yao,Aixin Sun,Yi Tay, "Deep Learning based Recommender System: A Survey and NewPerspectives", ACM Comput. Surv. 1, 1, Article 1 (July 2018), 35 pages.
[5] Paul Covington, Jay Adams, Emre Sargin, ” Deep Neural Networks for YouTube Recommendations”, Boston, Massachusetts, USA — September 15 - 19, 2016.
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