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Information Retrieval and Recommender Systems

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Data Science in Practice

Part of the book series: Studies in Big Data ((SBD,volume 46))

Abstract

This chapter provides a brief introduction to two of the most common applications of data science methods in e-commerce: information retrieval and recommender systems. First, a brief overview of the systems is presented followed by details on some of the most commonly applied models used for these systems and how these systems are evaluated. The chapter ends with an overview of some of the application areas in which information retrieval and recommender systems are typically developed.

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Notes

  1. 1.

    Initiative for the Evaluation of XML Retrieval, http://inex.mmci.uni-saarland.de.

  2. 2.

    http://www.netflixprize.com.

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Author information

Authors and Affiliations

  1. Iniversidad Autńoma de Madrid, Madrid, Spain

    Alejandro Bellogín

  2. University of Skövde, Skövde, Sweden

    Alan Said

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  1. Alejandro Bellogín
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Correspondence to Alan Said .

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Editors and Affiliations

  1. University of Skövde, Skövde, Sweden

    Alan Said

  2. University of Skövde, Skövde, Sweden

    Vicenç Torra

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Bellogín, A., Said, A. (2019). Information Retrieval and Recommender Systems. In: Said, A., Torra, V. (eds) Data Science in Practice. Studies in Big Data, vol 46. Springer, Cham. https://doi.org/10.1007/978-3-319-97556-6_5

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