Deep Neural Networks for YouTube Recommendations
Paul Covington, Jay Adams, Emre Sargin
Abstract
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.
Topics & Concepts
Deep learningComputer scienceRanking (information retrieval)Focus (optics)Deep neural networksArtificial intelligenceRecommender systemArtificial neural networkScale (ratio)Data scienceInformation retrievalMachine learningGeographyCartographyOpticsPhysicsRecommender Systems and TechniquesImage Retrieval and Classification TechniquesSentiment Analysis and Opinion Mining