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
MGAT: Multimodal Graph Attention Network for Recommendation
TL;DR: A new Multimodal Graph Attention Network, short for MGAT, is proposed, which disentangles personal interests at the granularity of modality and is able to capture more complex interaction patterns hidden in user behaviors and provide a more accurate recommendation.
Proceedings ArticleDOI
Fake Co-visitation Injection Attacks to Recommender Systems.
TL;DR: New attacks to recommender systems are proposed which can spoof a recommender system to make recommendations as an attacker desires and are modeled as constrained linear optimization problems by solving which the attacker can perform attacks with maximal threats.
Proceedings ArticleDOI
SlateQ: A Tractable Decomposition for Reinforcement Learning with Recommendation Sets
Eugene Ie,Vihan Jain,Jing Wang,Sanmit Narvekar,Ritesh Agarwal,Rui Wu,Heng-Tze Cheng,Tushar Deepak Chandra,Craig Boutilier +8 more
TL;DR: SLATEQ is developed, a decomposition of value-based temporal-difference and Q-learning that renders RL tractable with slates and shows that the long-term value of a slate can be decomposed into a tractable function of its component item-wise LTVs.
Proceedings Article
Learning Disentangled Representations for Recommendation
TL;DR: In this article, the authors present the MACRo-mIcro Disentangled Variational Auto-Encoder (MacridVAE) for learning disentangled representations from user behavior by inferring high-level concepts associated with user intentions (e.g., to buy a shirt or a cellphone), while capturing the preference of a user regarding the different concepts separately.
Book ChapterDOI
Recommender systems for health informatics: state-of-the-art and future perspectives
André Calero Valdez,André Calero Valdez,Martina Ziefle,Katrien Verbert,Alexander Felfernig,Andreas Holzinger +5 more
TL;DR: This work provides a three-part research framework to access health recommender systems, suggesting to incorporate domain understanding, evaluation and specific methodology into the development process.
References
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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