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

Authors:

Carlos A. Gomez-Uribe,

Neil HuntAuthors Info & Claims

ACM Transactions on Management Information Systems (TMIS), Volume 6, Issue 4

Article No.: 13, Pages 1 - 19

https://doi.org/10.1145/2843948

Published: 28 December 2015 Publication History

Abstract

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.

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Cited By

Boisvert SBélanger A(2024)Au-delà de Netflix : penser la diversité des pratiques et plateformes de télévision en ligneKinephanos10.7202/1113447ar10:1(1-14)Online publication date: 12-Sep-2024
https://doi.org/10.7202/1113447ar
Çiçeklioğlu A(2024)HABERE ÇEVRİM İÇİ ERİŞİMDE YENİ DÖNEM: KİŞİSELLEŞTİRİLMİŞ HABER UYGULAMALARIKritik İletişim Çalışmaları Dergisi10.53281/kritik.14383066:1(99-130)Online publication date: 2-Jul-2024
https://doi.org/10.53281/kritik.1438306
He SLi Y(2024)An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO AlgorithmsICST Transactions on Scalable Information Systems10.4108/eetsis.517611:5Online publication date: 8-Apr-2024
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Index Terms

The Netflix Recommender System: Algorithms, Business Value, and Innovation
1. Networks
  1. Network protocols

Recommendations

A Scalable, Accurate Hybrid Recommender System
WKDD '10: Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining

Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of recommender systems: collaborative filtering, content-based filtering, and ...
Acquiring User Information Needs for Recommender Systems
WI-IAT '13: Proceedings of the 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 03

Most recommender systems attempt to use collaborative filtering, content-based filtering or hybrid approach to recommend items to new users. Collaborative filtering recommends items to new users based on their similar neighbours, and content-based ...
Investigating serendipity in recommender systems based on real user feedback
SAC '18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing

Over the past several years, research in recommender systems has emphasized the importance of serendipity, but there is still no consensus on the definition of this concept and whether serendipitous items should be recommended is still not a well-...

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Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 6, Issue 4

January 2016

73 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/2869770

Editor:
Alexander Tuzhilin
New York University, USA

Issue’s Table of Contents

Copyright © 2015 Owner/Author.

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.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 December 2015

Accepted: 01 November 2015

Revised: 01 September 2015

Received: 01 July 2015

Published in TMIS Volume 6, Issue 4

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Cited By

Boisvert SBélanger A(2024)Au-delà de Netflix : penser la diversité des pratiques et plateformes de télévision en ligneKinephanos10.7202/1113447ar10:1(1-14)Online publication date: 12-Sep-2024
https://doi.org/10.7202/1113447ar
Çiçeklioğlu A(2024)HABERE ÇEVRİM İÇİ ERİŞİMDE YENİ DÖNEM: KİŞİSELLEŞTİRİLMİŞ HABER UYGULAMALARIKritik İletişim Çalışmaları Dergisi10.53281/kritik.14383066:1(99-130)Online publication date: 2-Jul-2024
https://doi.org/10.53281/kritik.1438306
He SLi Y(2024)An Improved Intelligent Machine Learning Approach to Music Recommendation Based on Big Data Techniques and DSO AlgorithmsICST Transactions on Scalable Information Systems10.4108/eetsis.517611:5Online publication date: 8-Apr-2024
https://doi.org/10.4108/eetsis.5176
Bharti IChauhan KAggarwal P(2024)Generative AIEnhancing Communication and Decision-Making With AI10.4018/979-8-3693-9246-1.ch001(1-36)Online publication date: 27-Sep-2024
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Sainger GIrfan M(2024)Roadmap to Talent ManagementBuilding Organizational Capacity and Strategic Management in Academia10.4018/979-8-3693-6967-8.ch017(463-492)Online publication date: 22-Nov-2024
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Mitra ADas S(2024)Exploring the Synergy of Artificial Intelligence and Big Data Analytics in Enhancing Customer Engagement StrategiesImproving Service Quality and Customer Engagement With Marketing Intelligence10.4018/979-8-3693-6813-8.ch008(196-210)Online publication date: 30-Jun-2024
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Garg NGupta KChelikani HRavada P(2024)The Evolution of Personalization From Traditional Marketing to AI ComputingAI for Large Scale Communication Networks10.4018/979-8-3693-6552-6.ch019(415-444)Online publication date: 25-Oct-2024
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Arockia Venice JArivazhagan DSuman NShanthi HSwadhi R(2024)Recommendation Systems and Content PersonalizationAI for Large Scale Communication Networks10.4018/979-8-3693-6552-6.ch015(323-348)Online publication date: 25-Oct-2024
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