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A Comprehensive Survey of Neighborhood-Based Recommendation Methods

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Recommender Systems Handbook

Abstract

Among collaborative recommendation approaches, methods based on nearest-neighbors still enjoy a huge amount of popularity, due to their simplicity, their efficiency, and their ability to produce accurate and personalized recommendations. This chapter presents a comprehensive survey of neighborhood-based methods for the item recommendation problem. In particular, the main benefits of such methods, as well as their principal characteristics, are described. Furthermore, this document addresses the essential decisions that are required while implementing a neighborhood-based recommender system, and gives practical information on how to make such decisions. Finally, the problems of sparsity and limited coverage, often observed in large commercial recommender systems, are discussed, and some solutions to overcome these problems are presented.

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Notes

  1. 1.

    The distribution of ratings in real-life data is normally skewed, i.e. most ratings are given to a small proportion of items.

  2. 2.

    http://www.grouplens.org/.

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Author information

Authors and Affiliations

  1. Computer Science Department, Purdue University, West Lafayette, IN, USA

    Xia Ning

  2. Software Engineering and IT Department, École de Technologie Supérieure, Montreal, QC, Canada

    Christian Desrosiers

  3. Computer Science and Engineering Department, University of Minnesota, Minneapolis, MN, USA

    George Karypis

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  1. Xia Ning
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  2. Christian Desrosiers
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Correspondence to Christian Desrosiers .

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Editors and Affiliations

  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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Ning, X., Desrosiers, C., Karypis, G. (2015). A Comprehensive Survey of Neighborhood-Based Recommendation Methods. In: Ricci, F., Rokach, L., Shapira, B. (eds) Recommender Systems Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7637-6_2

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