Mehmet Emre Sargin
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Deep Neural Networks for YouTube Recommendations
Paul Covington
Jay Adams
Proceedings of the 10th ACM Conference on Recommender Systems, ACM, New York, NY, USA (2016) (to appear)
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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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Boosting Video Classification Using Cross-Video Signals
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Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2011) (to appear)
Automatic Language Identification in Music Videos with Low Level Audio and Visual Features
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Vijay Chandrasekhar
Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2011)
Audiovisual Celebrity Recognition in Unconstrained Web Videos
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Pedro Moreno
Proceedings of the IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2009)
