Generating Recommendation Dialogs by Extracting Information from User Reviews

Recommendation dialog systems help users navigate e-commerce listings by asking questions about users’ preferences toward relevant domain attributes. We present a framework for generating and ranking fine-grained, highly relevant questions from user-generated reviews. We demonstrate our approach on a new dataset just released by Yelp, and release a new sentiment lexicon with 1329 adjectives for the restaurant domain.

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