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.