  Growing attention has been paid in Conversational Recommendation System
(CRS), which works as a conversation-based and recommendation task-oriented
tool to provide items of interest and explore user preference. However,
existing work in CRS fails to explicitly show the reasoning logic to users and
the whole CRS still remains a black box. Therefore we propose a novel
end-to-end framework named Explanation Generation for Conversational
Recommendation (EGCR) based on generating explanations for conversational
agents to explain why they make the action. EGCR incorporates user reviews to
enhance the item representation and increase the informativeness of the whole
conversation. To the best of our knowledge, this is the first framework for
explainable conversational recommendation on real-world datasets. Moreover, we
evaluate EGCR on one benchmark conversational recommendation datasets and
achieve better performance on both recommendation accuracy and conversation
quality than other state-of-the art models. Finally, extensive experiments
demonstrate that generated explanations are not only having high quality and
explainability, but also making CRS more trustworthy. We will make our code
available to contribute to the CRS community
