Difference between revisions of "2016 DeepNeuralNetworksforYoutubeRec"

Jump to: navigation, search
(Imported from text file)
m (Gmelli moved page Test:2016 DeepNeuralNetworksforYoutubeRec to 2016 DeepNeuralNetworksforYoutubeRec without leaving a redirect)
(No difference)

Revision as of 22:27, 26 March 2020

Subject Headings:


Cited By



YouTube represents one of the largest scale and most sophisticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and focus on the dramatic performance improvements brought by deep learning. The paper is split according to the classic two-stage information retrieval dichotomy: first, we detail a deep candidate generation model and then describe a separate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.



 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2016 DeepNeuralNetworksforYoutubeRecPaul Covington
Jay Adams
Emre Sargin
Deep Neural Networks for Youtube Recommendations2016
AuthorPaul Covington +, Jay Adams + and Emre Sargin +
titleDeep Neural Networks for Youtube Recommendations +
year2016 +