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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Product-Based Neural Networks for User Response Prediction over Multi-Field Categorical Data
TL;DR: Zhang et al. as discussed by the authors proposed Product-based Neural Network (PIN), which adopts a feature extractor to explore feature interactions and generalizes the kernel product to a net-in-net architecture.
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Recommendation system based on deep learning methods: a systematic review and new directions
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Multi-Interest Network with Dynamic Routing for Recommendation at Tmall
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Relational Collaborative Filtering: Modeling Multiple Item Relations for Recommendation
TL;DR: Wang et al. as discussed by the authors proposed Relational Collaborative Filtering (RCF) to exploit multiple item relations in recommender systems, and developed a two-level hierarchical attention mechanism to model user preference, where the first level attention discriminates which types of relations are more important and the second level attention considers specific relation values to estimate the contribution of a historical item.
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