Metrics for Evaluating Explainable Recommender Systems

Conference Paper (2023)
Author(s)

Joris Hulstijn (Université du Luxembourg)

Igor Tchappi (Université du Luxembourg)

Amro Najjar (Université du Luxembourg, Luxembourg Institute of Science and Technology)

Reyhan Aydoğan (TU Delft - Interactive Intelligence, Özyeğin University)

Research Group
Interactive Intelligence
Copyright
© 2023 Joris Hulstijn, Igor Tchappi, Amro Najjar, Reyhan Aydoğan
DOI related publication
https://doi.org/10.1007/978-3-031-40878-6_12
More Info
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Publication Year
2023
Language
English
Copyright
© 2023 Joris Hulstijn, Igor Tchappi, Amro Najjar, Reyhan Aydoğan
Research Group
Interactive Intelligence
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Pages (from-to)
212-230
ISBN (print)
9783031408779
Reuse Rights

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Abstract

Recommender systems aim to support their users by reducing information overload so that they can make better decisions. Recommender systems must be transparent, so users can form mental models about the system’s goals, internal state, and capabilities, that are in line with their actual design. Explanations and transparent behaviour of the system should inspire trust and, ultimately, lead to more persuasive recommendations. Here, explanations convey reasons why a recommendation is given or how the system forms its recommendations. This paper focuses on the question how such claims about effectiveness of explanations can be evaluated. Accordingly, we investigate various models that are used to assess the effects of explanations and recommendations. We discuss objective and subjective measurement and argue that both are needed. We define a set of metrics for measuring the effectiveness of explanations and recommendations. The feasibility of using these metrics is discussed in the context of a specific explainable recommender system in the food and health domain.

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