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.

RevCore: Review-Augmented Conversational Recommendation

Findings Papers - Dialog and Interactive Systems

technical paper

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