Metrics for evaluating explainable recommender systems

Name: Metrics for evaluating explainable recommender systems
Author: Hulstijn, J., Tchappi, I., Najjar, A., Aydoğan, Reyhan

İsim	Metrics for evaluating explainable recommender systems
Yazar	Hulstijn, J., Tchappi, I., Najjar, A., Aydoğan, Reyhan
Basım Tarihi:	2023
Basım Yeri	- Springer
Konu	Evaluation, Explainable AI, Metrics, Recommender systems
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-303140877-9
Kayıt Numarası	4c457b23-6adc-427e-9c8c-9baefad5474f
Lokasyon	Computer Science
Tarih	2023
Notlar	Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung ; Fonds National de la Recherche Luxembourg ; Ministero dell’Istruzione, dell’Università e della Ricerca ; TÜBİTAK
Örnek Metin	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.
DOI	10.1007/978-3-031-40878-6_12
Cilt	14127 LNAI

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Kaynağa git

Metrics for evaluating explainable recommender systems

Yazar Hulstijn, J., Tchappi, I., Najjar, A., Aydoğan, Reyhan

Basım Tarihi 2023

Basım Yeri - Springer

Konu Evaluation, Explainable AI, Metrics, Recommender systems

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-303140877-9

Kayıt Numarası 4c457b23-6adc-427e-9c8c-9baefad5474f

Lokasyon Computer Science

Tarih 2023

Notlar Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung ; Fonds National de la Recherche Luxembourg ; Ministero dell’Istruzione, dell’Università e della Ricerca ; TÜBİTAK

Örnek Metin 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.

DOI 10.1007/978-3-031-40878-6_12

Cilt 14127 LNAI