- Paul Covington
- Jay Adams
- Emre Sargin
Proceedings of the 10th ACM Conference on Recommender Systems, ACM, New York, NY, USA (2016) (to appear)
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.
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