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Deep Neural Networks for YouTube Recommendations Paul Covington, Jay Adams, Emre Sargin Google Mountain View, CA {pcovington, jka, msargin}@google.com ABSTRACT YouTube represents one of the largest scale and most sophis- ticated industrial recommendation systems in existence. In this paper, we describe the system at a high level and fo- cus 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 sepa- rate deep ranking model. We also provide practical lessons and insights derived from designing, iterating and maintain- ing a massive recommendation system with enormous user- facing impact. Keywords recommender system; deep learning; scalability 1. INTRODUCTION YouTube is the world’s largest platform for creating, shar- ing and discovering video content. YouTube recommenda- tions are responsible for helping more than a billion users discover personalized content from an ever-growing corpus of videos. In this paper we will focus on the immense im- pact deep learning has recently had on the YouTube video recommendations system. Figure 1 illustrates the recom- mendations on the YouTube mobile app home. Recommending YouTube videos is extremely challenging from three major perspectives: • Scale: Many existing recommendation algorithms proven to work well on small problems fail to operate on our scale. Highly specialized distributed learning algorithms and efficient serving systems are essential for handling YouTube’s massive user base and corpus. • Freshness: YouTube has a very dynamic corpus with many hours of video are uploaded per second. The recommendation system should be responsive enough to model newly uploaded content as well as the lat- est actions taken by the user. Balancing new content Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). RecSys ’16 September 15-19, 2016, Boston , MA, USA ⃝c 2016Copyrightheldbytheowner/author(s). ACM ISBN 978-1-4503-4035-9/16/09. DOI: http://dx.doi.org/10.1145/2959100.2959190 Figure 1: Recommendations displayed on YouTube mobile app home. with well-established videos can be understood from an exploration/exploitation perspective. • Noise: Historical user behavior on YouTube is inher- ently difficult to predict due to sparsity and a vari- ety of unobservable external factors. We rarely ob- tain the ground truth of user satisfaction and instead model noisy implicit feedback signals. Furthermore, metadata associated with content is poorly structured without a well defined ontology. Our algorithms need to be robust to these particular characteristics of our training data. In conjugation with other product areas across Google, YouTube has undergone a fundamental paradigm shift to- wards using deep learning as a general-purpose solution for nearly all learning problems. Our system is built on Google Brain [4] which was recently open sourced as TensorFlow [1]. TensorFlow provides a flexible framework for experimenting with various deep neural network architectures using large- scale distributed training. Our models learn approximately one billion parameters and are trained on hundreds of bil- lions of examples. In contrast to vast amount of research in matrix factoriza-PDF Image | Deep Neural Networks for YouTube Recommendations
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