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Encyclopedia of Big Data pp 1–4Cite as

Collaborative Filtering

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Abstract

Collaborative filtering (CF) is a process to filter information or patterns with collaboration among multiple agents and resources. The main idea of CF is to effectively extract useful information from the overwhelming amount of collected data. This article discusses the perception of CF techniques and explains how to utilize CF in a recommender system (RS). RS provides recommendations to an active user based on items that other similar users prefer. CF makes automatic predictions of a user’s interests by utilizing stored data of various users, which makes it a key method for RS.

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References

  • Aggarwal, C. C. (2016). An introduction to recommender systems. In Recommender systems (pp. 1–28). Cham: Springer.

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  • Koren, Y., & Bell, R. (2015). Advances in collaborative filtering. In Recommender systems handbook (pp. 77–118). Boston: Springer.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Idaho, Moscow, ID, USA

    Ashrf Althbiti & Xiaogang Ma

Authors
  1. Ashrf Althbiti
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  2. Xiaogang Ma
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Corresponding author

Correspondence to Ashrf Althbiti .

Editor information

Editors and Affiliations

  1. School of Policy, Government and International Affairs, George Mason University, Fairfax, VA, USA

    Laurie A. Schintler

  2. School of Policy, Government and International Affairs, George Mason University, Fairfax, VA, USA

    Connie L. McNeely

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Althbiti, A., Ma, X. (2019). Collaborative Filtering. In: Schintler, L., McNeely, C. (eds) Encyclopedia of Big Data. Springer, Cham. https://doi.org/10.1007/978-3-319-32001-4_274-1

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  • DOI: https://doi.org/10.1007/978-3-319-32001-4_274-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-32001-4

  • Online ISBN: 978-3-319-32001-4

  • eBook Packages: Springer Reference Business and ManagementReference Module Humanities and Social SciencesReference Module Business, Economics and Social Sciences

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