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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Meta-Graph Based Attention-Aware Recommendation over Heterogeneous Information Networks
TL;DR: A Meta-Graph based Attention-aware Recommendation (MGAR) over HINs, which utilizes rich meta-graph based latent features to guide the heterogeneous information fusion recommendation and proposes an attention-based feature enhancement model which enables useful features and useless features contribute differently to the prediction, thus improves the performance of the recommendation.
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References
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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