Deep Neural Networks for YouTube Recommendations
Paul Covington,Jay Adams,Emre Sargin +2 more
- pp 191-198
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TLDR
This paper details a deep candidate generation model and then describes a separate deep ranking model and provides practical lessons and insights derived from designing, iterating and maintaining a massive recommendation system with enormous user-facing impact.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.read more
Citations
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The Architectural Implications of Facebook's DNN-Based Personalized Recommendation
Udit Gupta,Carole-Jean Wu,Xiaodong Wang,Maxim Naumov,Brandon Reagen,David Brooks,Bradford Cottel,Kim Hazelwood,Mark Hempstead,Bill Jia,Hsien-Hsin S. Lee,Andrey Malevich,Dheevatsa Mudigere,Mikhail Smelyanskiy,Liang Xiong,Xuan Zhang +15 more
TL;DR: A set of real-world, production-scale DNNs for personalized recommendation coupled with relevant performance metrics for evaluation are presented and in-depth analysis is conducted that underpins future system design and optimization for at-scale recommendation.
References
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