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The Netflix Recommender System: Algorithms, Business Value, and Innovation

The Netflix Recommender System: Algorithms, Business Value, and Innovation The Netflix Recommender System: Algorithms, Business Value, and Innovation CARLOS A. GOMEZ-URIBE and NEIL HUNT, Netflix, Inc. This article discusses the various algorithms that make up the Netflix recommender system, and describes its business purpose. We also describe the role of search and related algorithms, which for us turns into a recommendations problem as well. We explain the motivations behind and review the approach that we use to improve the recommendation algorithms, combining A/B testing focused on improving member retention and medium term engagement, as well as offline experimentation using historical member engagement data. We discuss some of the issues in designing and interpreting A/B tests. Finally, we describe some current areas of focused innovation, which include making our recommender system global and language aware. Categories and Subject Descriptors: C.2.2 [Recommender Systems]: Machine Learning General Terms: Algorithms, Recommender Systems, A/B Testing, Product Innovation Additional Key Words and Phrases: Recommender systems ACM Reference Format: Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6, 4, Article 13 (December 2015), 19 pages. DOI: http://dx.doi.org/10.1145/2843948 1. INTRODUCTION Storytelling has always been at the core of human nature. Major technological http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

The Netflix Recommender System: Algorithms, Business Value, and Innovation

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2015 by ACM Inc.
ISSN
2158-656X
DOI
10.1145/2843948
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Abstract

The Netflix Recommender System: Algorithms, Business Value, and Innovation CARLOS A. GOMEZ-URIBE and NEIL HUNT, Netflix, Inc. This article discusses the various algorithms that make up the Netflix recommender system, and describes its business purpose. We also describe the role of search and related algorithms, which for us turns into a recommendations problem as well. We explain the motivations behind and review the approach that we use to improve the recommendation algorithms, combining A/B testing focused on improving member retention and medium term engagement, as well as offline experimentation using historical member engagement data. We discuss some of the issues in designing and interpreting A/B tests. Finally, we describe some current areas of focused innovation, which include making our recommender system global and language aware. Categories and Subject Descriptors: C.2.2 [Recommender Systems]: Machine Learning General Terms: Algorithms, Recommender Systems, A/B Testing, Product Innovation Additional Key Words and Phrases: Recommender systems ACM Reference Format: Carlos A. Gomez-Uribe and Neil Hunt. 2015. The Netflix recommender system: Algorithms, business value, and innovation. ACM Trans. Manage. Inf. Syst. 6, 4, Article 13 (December 2015), 19 pages. DOI: http://dx.doi.org/10.1145/2843948 1. INTRODUCTION Storytelling has always been at the core of human nature. Major technological

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ACM Transactions on Management Information Systems (TMIS) – Association for Computing Machinery

Published: Dec 28, 2015

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