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Recommender Systems in Industry: A Netflix Case Study

  • Chapter
Recommender Systems Handbook

Abstract

The Netflix Prize put a spotlight on the importance and use of recommender systems in real-world applications. Many the competition provided many lessons about how to approach recommendation and many more have been learned since the Grand Prize was awarded in 2009. The evolution of industrial applications of recommender systems has been driven by the availability of different kinds of user data and the level of interest for the area within the research community. The goal of this chapter is to give an up-to-date overview of recommender systems techniques used in an industrial setting. We will give a high-level description the practical use of recommendation and personalization techniques. We will highlight some of the main lessons learned from the Netflix Prize. We will then use Netflix personalization as a case study to describe several approaches and techniques used in a real-world recommendation system. Finally, we will pinpoint what we see as some promising current research avenues and unsolved problems that deserve attention in this domain from an industry perspective.

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Notes

  1. 1.

    The application of Matrix Factorization to the task of rating prediction closely resembles the technique known as Singular Value Decomposition used, for example, to identify latent factors in Information Retrieval. Therefore, it is common to see people referring to this MF solution as SVD.

  2. 2.

    For practical purposes we consider responses below a few hundred milliseconds (e.g. 200) to be real-time.

  3. 3.

    Intermediate recommendations usually represent lists of items that have been pre-selected and even ranked in advanced but need to undergo further processing such as filtering or re-ranking before being presented to the user.

  4. 4.

    Chukwa is a Hadoop subproject devoted to large-scale log collection and analysis.

  5. 5.

    Hadoop is an open-source software framework for storage and large-scale processing of data-sets on clusters of commodity hardware.

  6. 6.

    Apache Hive is a data warehouse infrastructure built on top of Hadoop for providing data summarization, query, and analysis.

  7. 7.

    Pig is a high-level platform for creating MapReduce programs used with Hadoop using a language called Pig Latin.

  8. 8.

    The Hadoop Distributed File System (HDFS) is a distributed file system designed to run on commodity hardware.

  9. 9.

    Amazon S3 (Simple Storage Service) is an online file storage web service offered by Amazon Web Services.

  10. 10.

    Apache Kafka is publish-subscribe messaging rethought as a distributed commit log.

  11. 11.

    Apache Cassandra is an open source distributed database management system designed to handle large amounts of data across many commodity servers, providing high availability with no single point of failure.

  12. 12.

    EVCache is a distributed in-memory data store for the cloud.

  13. 13.

    MySQL is one of the most popular open source relational databases.

  14. 14.

    It is important to note that the term “social recommendation” was originally used to describe collaborative filtering approaches [7, 74].

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  1. Netflix, 100 Winchester Cr., Los Gatos, CA, 95032, USA

    Xavier Amatriain & Justin Basilico

  2. Quora, 150 Castro St., Mountain View, USA

    Xavier Amatriain

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  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bolzano - Bozen, Italy

    Francesco Ricci

  2. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Lior Rokach

  3. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Bracha Shapira

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Amatriain, X., Basilico, J. (2015). Recommender Systems in Industry: A Netflix Case Study. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_11

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