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Fuzzy Synthetic Method for Evaluating Explanations in Recommender Systems by Zhong, Jinfeng; Negre, Elsa is a scholarly article available to read on EtoBox.

What is Fuzzy Synthetic Method for Evaluating Explanations in Recommender Systems about?

Recommender systems aim to help users find relevant items more quickly by providing personalized recommendations. Explanations in recommender systems help users understand why such recommendations have been generated, which in turn makes the system more transparent and promotes users' trust and satisfaction. In recent years, explaining recommendations has drawn increasing attention from both academia and from industry. In this paper, we present a user study to investigate context-aware explanations in recommender systems. In particular, we build a web-based questionnaire that is able to interact with users: generating and explaining recommendations. With this questionnaire, we investigate the effects of context-aware explanations in terms of efficiency, effectiveness, persuasiveness, satisfaction, trust and transparency through a user study. Besides, we propose a novel method based on fuzzy synthetic evaluation for aggregating these metrics.

Author

Zhong, Jinfeng; Negre, Elsa

Published

2024

Language

EN