Author
Hulstijn, J., Tchappi, I., Najjar, A., Aydoğan, Reyhan
Publication Date
2023
Publication Place
-
Springer
Subject
Evaluation, Explainable AI, Metrics, Recommender systems
Type
Document
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-303140877-9
Record ID
4c457b23-6adc-427e-9c8c-9baefad5474f
Library Location
Computer Science
Date
2023
Notes
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung ; Fonds National de la Recherche Luxembourg ; Ministero dell’Istruzione, dell’Università e della Ricerca ; TÜBİTAK
Sample Text
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
