  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.
