Jingjing Liu, Stephanie Seneff, and Victor Zue
In this paper we present a framework for spoken recommendation systems. To provide reliable recommendations to users, we incorporate a review summarization technique which extracts informative opinion summaries from grass-roots users ‘ reviews. The dialogue system then utilizes these review summaries to support both quality-based opinion inquiry and feature-specific entity search. We propose a probabilistic language generation approach to automatically creating recommendations in spoken natural language from the text-based opinion summaries. A user study in the restaurant domain shows that the proposed approaches can effectively generate reliable and helpful recommendations in human-computer conversations.