Unpacking YouTube's Recommender System

Over the past couple of years, YouTube has come under fire for its recommender system, with the media suggesting that it is promoting violent content, or banning LGBT content for violating its terms of service. Seemingly in response to all of this, Google has finally released a paper explaining YouTube's recommender system, including how it makes recommendations and the information it gathers in doing so. The paper, by Zhe Zhao, Lichan Hong, Li Wei, Jilin Chen, Aniruddh Nath, Shawn Andrews, Aditee Kumthekar, Maheswaran Sathiamoorthy, Xinyang Yi, and Ed Chi, discusses some of the problems that common/"normal" recommender systems face, some of the specific ones that a platform as big as YouTube faces, and the architecture they used to create their system. One of the biggest issues the program had to tackle was that of scalability. Basically, no other recommender system has to work with such a large user platform, or with so many individual pieces of content.