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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McDRAM v2: In-Dynamic Random Access Memory Systolic Array Accelerator to Address the Large Model Problem in Deep Neural Networks on the Edge
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One Person, One Model, One World: Learning Continual User Representation without Forgetting
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Predicting yield performance of parents in plant breeding: A neural collaborative filtering approach
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Proceedings ArticleDOI
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems
TL;DR: Zhang et al. as discussed by the authors theoretically proved that contrastive loss is equivalent to reducing the exposure bias via inverse propensity weighting, which provides a new perspective for understanding the effectiveness of contrastive learning.
Journal ArticleDOI
Near-Memory Processing in Action: Accelerating Personalized Recommendation with AxDIMM
Liu Ke,Xuan Zhang,Jinin So,Jong Geon Lee,Shin-haeng Kang,Sukhan Lee,Han Songyi,Yeongon Cho,Jin-Hyun Kim,Yongsuk Kwon,Kyung-Soo Kim,Jin Jung,Il-Kwon Yun,Sung Joo Park,Hyunsun Park,Joon-Ho Song,Jeonghyeon Cho,Kyomin Sohn,Nam Sung Kim,Hsien-Hsin S. Lee +19 more
TL;DR: This work developed a scalable, practical DIMM-based NMP solution tailor-designed for accelerating the inference serving of personalized recommendation system using industry-representative recommendation framework and experimentally validated the performance of a two-ranked AxDIMM prototype.
References
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