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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Proceedings ArticleDOI
TwHIN: Embedding the Twitter Heterogeneous Information Network for Personalized Recommendation
Ahmed El-Kishky,Thomas Markovich,S. Park,Chetan Verma,Baekjin Kim,Ramy Eskander,Yury Malkov,Frank Portman,Sofı́a Samaniego,Yingyuan Xiao,Aria Haghighi +10 more
TL;DR: This work investigates knowledge-graph embeddings for entities in the Twitter HIN (TwHIN) and shows that these pretrained representations yield significant offline and online improvement for a diverse range of downstream recommendation and classification tasks: personalized ads rankings, account follow-recommendation, offensive content detection, and search ranking.
Journal ArticleDOI
Tubes and bubbles topological confinement of YouTube recommendations.
TL;DR: In this paper, the authors investigate the role of recommendation algorithms in online user confinement and show that the landscape of mean-field YouTube recommendations is often prone to confinement dynamics, and that the most confined recommendation graphs seem to be organized around sets of videos that garner the highest audience and thus plausibly viewing time.
Journal ArticleDOI
Estimating Attention Flow in Online Video Networks
TL;DR: In this paper, a YouTube video network with 60,740 music videos interconnected by the recommendation links is constructed, and a bow-tie structure is used to characterize the Vevo network and the core component (23.1% of the videos) occupies most of the attention.
Posted Content
Image Matters: Visually modeling user behaviors using Advanced Model Server
Tiezheng Ge,Liqin Zhao,Guorui Zhou,Keyu Chen,Shuying Liu,Huimin Yi,Zelin Hu,Bochao Liu,Peng Sun,Haoyu Liu,Pengtao Yi,Sui Huang,Zhiqiang Zhang,Xiaoqiang Zhu,Yu Zhang,Kun Gai +15 more
TL;DR: This work proposes a novel and efficient distributed machine learning paradigm called Advanced Model Server (AMS), designed to be capable of learning a unified image descriptor model shared by all server nodes which embeds large images into low dimensional high level features before transmitting images to worker nodes, and proposes a Deep Image CTR Model.
References
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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