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Recommender Systems Over Data Streams

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Encyclopedia of Big Data Technologies

Abstract

Recommender systems predict preferences of users to offer them relevant items in case the selection is too large. Recommender systems have to serve in online environments that can be highly nonstationary. Traditional recommender algorithms may periodically rebuild their models, but they cannot adjust to quick changes in trends caused, for example, by timely information. In this article, we investigate online learning based recommender algorithms that can efficiently handle nonstationary datasets. We show that online learning for recommendation is rather the usual than the exceptional task: For example, if no user history is available, we have to build a user model on the fly, based on the interactions in the live user session. To the best of our knowledge, this is the first survey with a comprehensive overview of the ideas for recommendation over streaming data and their implementation in various distributed data stream processing systems.

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Acknowledgements

Support from the EU H2020 grant Streamline No 688191 and the “Big Data—Momentum” grant of the Hungarian Academy of Sciences.

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Authors and Affiliations

  1. Institute for Computer Science and Control, Hungarian Academy of Sciences (MTA SZTAKI), Budapest, Hungary

    András A. Benczúr & Levente Kocsis

  2. Department of Computer Science, Stanford University, Stanford, CA, USA

    Róbert Pálovics

Authors
  1. András A. Benczúr
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  2. Levente Kocsis
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  3. Róbert Pálovics
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Corresponding author

Correspondence to András A. Benczúr .

Editor information

Editors and Affiliations

  1. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Sherif Sakr

  2. Sch of Info Techno, Building J12, University of Sydney Sch of Info Techno, Building J12, Sydney, Australia

    Albert Zomaya

Section Editor information

  1. Politecnico di Milano http://home.deib.polimi.it/margara/

    Alessandro Margara

  2. Database Systems and Information Management Group, Technische Universität Berlin, Einsteinufer 17, 10587, Berlin, Germany

    Tilmann Rabl

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Benczúr, A.A., Kocsis, L., Pálovics, R. (2018). Recommender Systems Over Data Streams. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_328-1

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  • DOI: https://doi.org/10.1007/978-3-319-63962-8_328-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63962-8

  • Online ISBN: 978-3-319-63962-8

  • eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering

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