Deep Neural Networks for YouTube Recommendations
Paul Covington,Jay Adams,Emre Sargin +2 more
- pp 191-198
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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Proceedings ArticleDOI
Contrastive Learning for Sequential Recommendation
TL;DR: A novel multi-task framework called Contrastive Learning for Sequential Recommendation (CL4SRec) is proposed, which not only takes advantage of the traditional next item prediction task but also utilizes the contrastive learning framework to derive self-supervision signals from the original user behavior sequences.
Proceedings ArticleDOI
Explore, exploit, and explain: personalizing explainable recommendations with bandits
James McInerney,Benjamin Lacker,Samantha Hansen,Karl Higley,Hugues Bouchard,Alois Gruson,Rishabh Mehrotra +6 more
TL;DR: This work provides the first method that combines bandits and explanations in a principled manner and is able to jointly learn which explanations each user responds to; learn the best content to recommend for each user; and balance exploration with exploitation to deal with uncertainty.
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Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
TL;DR: This paper explores the problem of building ML systems that fail loudly, investigating methods for detecting dataset shift, identifying exemplars that most typify the shift, and quantifying shift malignancy, and demonstrates that domain-discriminating approaches tend to be helpful for characterizing shifts qualitatively and determining if they are harmful.
Proceedings ArticleDOI
Learning Tree-based Deep Model for Recommender Systems
TL;DR: A novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks is proposed and can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction.
Proceedings ArticleDOI
RecNMP: accelerating personalized recommendation with near-memory processing
Liu Ke,Udit Gupta,Benjamin Youngjae Cho,David Brooks,Vikas Chandra,Utku Diril,Amin Firoozshahian,Kim Hazelwood,Bill Jia,Hsien-Hsin S. Lee,Meng Li,Bert Maher,Dheevatsa Mudigere,Maxim Naumov,Martin Schatz,Mikhail Smelyanskiy,Xiaodong Wang,Brandon Reagen,Carole-Jean Wu,Mark Hempstead,Xuan Zhang +20 more
TL;DR: RecNMP as mentioned in this paper proposes a lightweight, commodity DRAM compliant, near-memory processing solution to accelerate personalized recommendation inference, which is specifically tailored to production environments with heavy co-location of operators on a single server.
References
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