Generating Recommendation Dialogs by Extracting Information from User Reviews
نویسندگان
چکیده
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.
منابع مشابه
NATIONAL UNIVERSITY OF SINGAPORE School of Computing PH.D DEFENCE - PUBLIC SEMINAR Title: Using Meta-Data from Free-Text User-Generated Content to Improve Personalized Recommendation by Reducing Sparsity Speaker: Mr
Recommender Systems (RS) have become increasingly essential in many domains for alleviating the "information overload" problem, but existing recommendation techniques suffer from the sparsity problem due to insufficient input data. In this thesis, we aim at extracting and incorporating meta-data from free-text UserGenerated Content (UGC) to lessen the effects of sparsity and therefore improve t...
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