  Existing conversational recommendation (CR) systems usually suffer from
insufficient item information when conducted on short dialogue history and
unfamiliar items. Incorporating external information (e.g., reviews) is a
potential solution to alleviate this problem. Given that reviews often provide
a rich and detailed user experience on different interests, they are potential
ideal resources for providing high-quality recommendations within an
informative conversation. In this paper, we design a novel end-to-end
framework, namely, Review-augmented Conversational Recommender (RevCore), where
reviews are seamlessly incorporated to enrich item information and assist in
generating both coherent and informative responses. In detail, we extract
sentiment-consistent reviews, perform review-enriched and entity-based
recommendations for item suggestions, as well as use a review-attentive
encoder-decoder for response generation. Experimental results demonstrate the
superiority of our approach in yielding better performance on both
recommendation and conversation responding.
