%0 Conference Paper %1 conf/recsys/CovingtonAS16 %A Covington, Paul %A Adams, Jay %A Sargin, Emre %B RecSys %D 2016 %E Sen, Shilad %E Geyer, Werner %E Freyne, Jill %E Castells, Pablo %I ACM %K %P 191-198 %R 10.1145/2959100.2959190 %T Deep Neural Networks for YouTube Recommendations %U http://research.google.com/pubs/archive/45530.pdf %X YouTube represents one of the largest scale and most sophis- ticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and fo- cus 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 sepa- rate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintain- ing a massive recommendation system with enormous user- facing impact. %@ 978-1-4503-4035-9

@inproceedings{conf/recsys/CovingtonAS16, abstract = {YouTube represents one of the largest scale and most sophis- ticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and fo- cus 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 sepa- rate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintain- ing a massive recommendation system with enormous user- facing impact.}, added-at = {2016-11-23T23:45:10.000+0100}, author = {Covington, Paul and Adams, Jay and Sargin, Emre}, biburl = {https://www.bibsonomy.org/bibtex/2365383100f66816d3e1028d4802db12b/nosebrain}, booktitle = {RecSys}, doi = {10.1145/2959100.2959190}, editor = {Sen, Shilad and Geyer, Werner and Freyne, Jill and Castells, Pablo}, interhash = {7c8fd2c2fa6bfa835f9eb5fae5278982}, intrahash = {365383100f66816d3e1028d4802db12b}, isbn = {978-1-4503-4035-9}, keywords = {}, pages = {191-198}, publisher = {ACM}, timestamp = {2016-11-23T23:45:10.000+0100}, title = {Deep Neural Networks for YouTube Recommendations}, url = {http://research.google.com/pubs/archive/45530.pdf}, year = 2016 }