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Recommendation Systems and the Use of Machine Learning Methods

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Abstract

Recommendation systems use the preferences of a user to provide him with individual content. For this purpose, information such as product features or product ratings is used to generate personalized recommendations. Recommendation systems thus represent a special form of personalization and offer enormous potential for companies, especially in connection with large information stocks. This article deals with an application-oriented presentation of the different concepts that can be used to create personalized recommendations. Each of these concepts contains an algorithm in its core, which can be implemented in many systems by means of machine learning. In this context, methods of machine learning for the creation of personalized recommendations are presented.

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Change history

  • 18 April 2023

    The sequence of authors was incorrect in the initially published version. It has been corrected.

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Author information

Authors and Affiliations

  1. Harthausen, Germany

    Andreas Peuker

  2. Homburg/Saar, Germany

    Thomas Barton

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  1. Andreas Peuker
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  2. Thomas Barton
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Editors and Affiliations

  1. FB Informatik, Hochschule Worms, Worms, Germany

    Thomas Barton

  2. FB Wirtschaft, Informatik, Recht (WIR), Technische Hochschule Wildau, Wildau, Germany

    Christian Müller

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Peuker, A., Barton, T. (2023). Recommendation Systems and the Use of Machine Learning Methods. In: Barton, T., Müller, C. (eds) Apply Data Science. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-38798-3_6

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  • DOI: https://doi.org/10.1007/978-3-658-38798-3_6

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  • Publisher Name: Springer Vieweg, Wiesbaden

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  • Online ISBN: 978-3-658-38798-3

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