2024/07/02 by Jinfeng Zhong, Zhong, Jinfeng, Elsa Negre +1
Computer Science · #Advanced Text Analysis Techniques #Educational Technology and Pedagogy #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Recommender Systems and Techniques
paper · pdf · doi:10.48550/arxiv.2407.02065
openalex publication_date 2024/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.