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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Journal ArticleDOI
Learning to Recommend With Multiple Cascading Behaviors
Chen Gao,Xiangnan He,Dahua Gan,Xiangning Chen,Fuli Feng,Yong Li,Tat-Seng Chua,Lina Yao,Yang Song,Depeng Jin +9 more
TL;DR: In this article, a multi-task learning framework is proposed for learning recommender systems from user multi-behavior data, where the optimization on a behavior is treated as a task.
Proceedings Article
Beyond Greedy Ranking: Slate Optimization via List-CVAE
TL;DR: List Conditional Variational Auto-Encoders (List-CVAE), which learns the joint distribution of documents on the slate conditioned on user responses, and directly generates full slates, and outperforms popular comparable ranking methods consistently on various scales of documents corpora.
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Personalised Reranking of Paper Recommendations Using Paper Content and User Behavior
TL;DR: This article examines an academic paper recommender that sends out paper recommendations in email newsletters, based on the users’ browsing history on the academic search engine, and proposes an approach to reranking candidate recommendations that utilizes both paper content and user behavior.
Proceedings ArticleDOI
PURE: Positive-Unlabeled Recommendation with Generative Adversarial Network
TL;DR: Zhang et al. as discussed by the authors developed a novel framework named PURE, which trains an unbiased positive-unlabeled discriminator to distinguish the true relevant user-item pairs against the ones that are non-relevant.
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Are Labels Required for Improving Adversarial Robustness
Jonathan Uesato,Jean-Baptiste Alayrac,Po-Sen Huang,Robert Stanforth,Alhussein Fawzi,Pushmeet Kohli +5 more
TL;DR: In this paper, the authors show that unlabeled data can be a competitive alternative to labeled data for training adversarially robust models, and they show that in a simple statistical setting, the sample complexity for learning an adversarial robust model from unlabelled data matches the fully supervised case up to constant factors.
References
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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