2016 TheNetflixRecommenderSystemAlgo

• (Gomez-Uribe & Hunt, 2016) ⇒ Carlos A. Gomez-Uribe, and Neil Hunt. (2016). “The Netflix Recommender System: Algorithms, Business Value, and Innovation.” In: ACM Transactions on Management Information Systems (TMIS) Journal, 6(4). doi:10.1145/2843948

Subject Headings: Netflix Movie Recommender.

Cited By

• http://scholar.google.com/scholar?q=%222016%22+The+Netflix+Recommender+System%3A+Algorithms%2C+Business+Value%2C+and+Innovation
• http://dl.acm.org/citation.cfm?id=2869770.2843948&preflayout=flat#citedby

Quotes

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 TheNetflixRecommenderSystemAlgo	Carlos A. Gomez-Uribe Neil Hunt			The Netflix Recommender System: Algorithms, Business Value, and Innovation				10.1145/2843948		2016