论文标题
测量推荐系统中的“为什么”：一项关于评估可解释建议的综合调查
Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation
论文作者
论文摘要
可解释的建议表明，它在提高建议性说服力，用户满意度，系统透明度等方面具有巨大的优势。可解释建议的一个基本问题是如何评估解释。在过去的几年中，已经提出了各种评估策略。但是，它们散布在不同的论文中，并且缺乏系统的比较。为了弥合这一差距，在本文中，我们全面审查了先前的工作，并根据评估的观点和评估方法为它们提供不同的分类法。除了总结先前的工作外，我们还分析了现有评估方法的（DIS）优势，并提供了有关如何选择它们的一系列准则。该调查的内容基于IJCAI，AAAI，TheWebConf，Recsys，UMAP和IUI等顶级会议的100多篇论文，它们的完整摘要在https://shimo.im.im/sheets/vkrpytcwvh6kxgdy/modoc/上提供。通过这项调查，我们最终旨在对可解释建议的评估进行清晰而全面的审查。
Explainable recommendation has shown its great advantages for improving recommendation persuasiveness, user satisfaction, system transparency, among others. A fundamental problem of explainable recommendation is how to evaluate the explanations. In the past few years, various evaluation strategies have been proposed. However, they are scattered in different papers, and there lacks a systematic and detailed comparison between them. To bridge this gap, in this paper, we comprehensively review the previous work, and provide different taxonomies for them according to the evaluation perspectives and evaluation methods. Beyond summarizing the previous work, we also analyze the (dis)advantages of existing evaluation methods and provide a series of guidelines on how to select them. The contents of this survey are based on more than 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys, UMAP, and IUI, and their complete summarization are presented at https://shimo.im/sheets/VKrpYTcwVH6KXgdy/MODOC/. With this survey, we finally aim to provide a clear and comprehensive review on the evaluation of explainable recommendation.
