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Basic Approaches in Recommendation Systems

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Recommendation Systems in Software Engineering

Abstract

Recommendation systems support users in finding items of interest. In this chapter, we introduce the basic approaches of collaborative filtering, content-based filtering, and knowledge-based recommendation. We first discuss principles of the underlying algorithms based on a running example. Thereafter, we provide an overview of hybrid recommendation approaches which combine basic variants. We conclude this chapter with a discussion of newer algorithmic trends, especially critiquing-based and group recommendation.

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Notes

  1. 1.

    For simplicity we assume k = 1 throughout this chapter.

  2. 2.

    Knowledge-based recommendation approaches based on the determination of similarities between items will be discussed in Sect. 2.7.

  3. 3.

    A detailed discussion of the application of MAUT in knowledge-based recommendation scenarios can be found in Ardissono et al. [1] and Felfernig et al. [16, 18].

  4. 4.

    If two or more items have the same overall score, a possibility is to force a decision by lot; where needed, this approach can also be applied by other hybrid recommendation approaches.

  5. 5.

    The impact of a wrong decision (selection) is rather low, therefore users invest less evaluation effort in a purchase situation.

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Authors and Affiliations

  1. Institute for Software Technology, Graz University of Technology, Inffeldgasse 16b/2, 8010, Graz, Austria

    Alexander Felfernig, Michael Jeran, Gerald Ninaus, Florian Reinfrank, Stefan Reiterer & Martin Stettinger

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  1. Alexander Felfernig
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  2. Michael Jeran
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  3. Gerald Ninaus
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Correspondence to Alexander Felfernig .

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Editors and Affiliations

  1. McGill University, Montréal, Québec, Canada

    Martin P. Robillard

  2. University of Hamburg, Hamburg, Germany

    Walid Maalej

  3. University of Calgary, Calgary, Alberta, Canada

    Robert J. Walker

  4. Microsoft Research, Redmond, Washington, USA

    Thomas Zimmermann

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Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., Stettinger, M. (2014). Basic Approaches in Recommendation Systems. In: Robillard, M., Maalej, W., Walker, R., Zimmermann, T. (eds) Recommendation Systems in Software Engineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45135-5_2

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