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Dimensions and metrics for evaluating recommendation systems

Version 2 2024-06-06, 11:48
Version 1 2016-03-01, 13:35
chapter
posted on 2024-06-06, 11:48 authored by I Avazpour, T Pitakrat, L Grunske, J Grundy
Recommendation systems support users and developers of various computer and software systems to overcome information overload, perform information discovery tasks, and approximate computation, among others. They have recently become popular and have attracted a wide variety of application scenarios ranging from business process modeling to source code manipulation. Due to this wide variety of application domains, different approaches and metrics have been adopted for their evaluation. In this chapter, we review a range of evaluation metrics and measures as well as some approaches used for evaluating recommendation systems. The metrics presented in this chapter are grouped under sixteen different dimensions, e.g., correctness, novelty, coverage. We review these metrics according to the dimensions to which they correspond. A brief overview of approaches to comprehensive evaluation using collections of recommendation system dimensions and associated metrics is presented. We also provide suggestions for key future research and practice directions.

History

Chapter number

10

Pagination

245-273

ISBN-13

9783642451348

Language

eng

Publication classification

B Book chapter, B1.1 Book chapter

Copyright notice

2014, Springer

Extent

19

Editor/Contributor(s)

Robillard MP, Maalej W, Walker RJ, Zimmermann T

Publisher

Springer

Place of publication

Berlin, Germany

Title of book

Recommendation systems in software engineering

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