  Explanations in conventional recommender systems have demonstrated benefits
in helping the user understand the rationality of the recommendations and
improving the system's efficiency, transparency, and trustworthiness. In the
conversational environment, multiple contextualized explanations need to be
generated, which poses further challenges for explanations. To better measure
explainability in conversational recommender systems (CRS), we propose ten
evaluation perspectives based on concepts from conventional recommender systems
together with the characteristics of CRS. We assess five existing CRS benchmark
datasets using these metrics and observe the necessity of improving the
explanation quality of CRS. To achieve this, we conduct manual and automatic
approaches to extend these dialogues and construct a new CRS dataset, namely
Explainable Recommendation Dialogues (E-ReDial). It includes 756 dialogues with
over 2,000 high-quality rewritten explanations. We compare two baseline
approaches to perform explanation generation based on E-ReDial. Experimental
results suggest that models trained on E-ReDial can significantly improve
explainability while introducing knowledge into the models can further improve
the performance. GPT-3 in the in-context learning setting can generate more
realistic and diverse movie descriptions. In contrast, T5 training on E-ReDial
can better generate clear reasons for recommendations based on user
preferences. E-ReDial is available at https://github.com/Superbooming/E-ReDial.
