推荐系统中解释评估的模糊综合方法
Fuzzy synthetic method for evaluating explanations in recommender systems
AI总结：
本文通过可交互的网络问卷开展用户研究，从效率、有效性、说服力、满意度、信任与透明度评估情境感知推荐解释，并提出基于模糊综合评价的指标聚合方法。
摘要：
推荐系统旨在通过提供个性化推荐，帮助用户更快地找到相关物品。推荐系统中的解释可帮助用户理解为何会生成此类推荐，从而提高系统透明度，并增进用户的信任与满意度。近年来，对推荐进行解释已受到学术界和工业界越来越多的关注。本文开展了一项用户研究，以考察推荐系统中的情境感知解释。具体而言，我们构建了一个能够与用户交互的基于网络的问卷：生成推荐并对其进行解释。借助该问卷，我们通过用户研究，从效率、有效性、说服力、满意度、信任和透明度等方面考察情境感知解释的影响。此外，我们提出了一种基于模糊综合评价的新方法，用于聚合这些指标。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
