  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.
