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    • Deep Neural Networks for YouTube Recommendations.

    Paul Covington,Jay Adams,Emre Sargin

    Proceedings of the 10th ACM Conference on Recommender Systems（2016）

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    Cited 4251|Views576
    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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    recommender system,deep learning,scalability
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    Chat Paper
    【要点】：本文介绍了YouTube推荐系统，并重点讨论了深度学习带来的显著性能提升，创新点在于运用了两阶段信息检索的经典范式，分别采用深度候选生成模型和深度排序模型。
    

    【方法】：方法包括设计并迭代维护一个大规模推荐系统，该系统面临巨大的用户影响。
    

    【实验】：文中提供了设计、迭代和维护一个具有巨大用户面向影响的推荐系统的实践经验和洞察力，但没有具体说明实验数据集名称和结果。
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