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Explainable recommendation has shown its great advantages for improvingrecommendation persuasiveness, user satisfaction, system transparency, amongothers. A fundamental problem of explainable recommendation is how to evaluatethe explanations. In the past few years, various evaluation strategies havebeen proposed. However, they are scattered in different papers, and there lacksa systematic and detailed comparison between them. To bridge this gap, in thispaper, we comprehensively review the previous work, and provide differenttaxonomies for them according to the evaluation perspectives and evaluationmethods. Beyond summarizing the previous work, we also analyze the(dis)advantages of existing evaluation methods and provide a series ofguidelines on how to select them. The contents of this survey are based on morethan 100 papers from top-tier conferences like IJCAI, AAAI, TheWebConf, Recsys,UMAP, and IUI, and their complete summarization are presented athttps://shimo.im/sheets/VKrpYTcwVH6KXgdy/MODOC/. With this survey, we finallyaim to provide a clear and comprehensive review on the evaluation ofexplainable recommendation.

Measuring "Why" in Recommender Systems: a Comprehensive Survey on the Evaluation of Explainable Recommendation