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Open AccessProceedings ArticleDOI

Deep Neural Networks for YouTube Recommendations

Paul Covington, +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.

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Citations
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Temporal Hierarchical Attention at Category- and Item-Level for Micro-Video Click-Through Prediction

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DAPPLE: a pipelined data parallel approach for training large models

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Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation

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Journal ArticleDOI

User-Personalized Review Rating Prediction Method Based on Review Text Content and User-Item Rating Matrix

TL;DR: This work proposes a user-personalized review rating prediction method by integrating the review text and user-item rating matrix information, which can significantly outperform the state-of-the-art methods.
References
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Book ChapterDOI

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

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Distributed Representations of Words and Phrases and their Compositionality

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

TL;DR: Batch Normalization as mentioned in this paper normalizes layer inputs for each training mini-batch to reduce the internal covariate shift in deep neural networks, and achieves state-of-the-art performance on ImageNet.
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Distributed Representations of Words and Phrases and their Compositionality

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