Microsoft Technology Licensing, LLC

Jay Banerjee

Kristina Nikolova Toutanova

Pallavi Choudhury

Margaret Ann Mitchell

Susan Michele Hendrich

Mouni Reddy

Xuetao Yin

Bill Dolan

Liang Xu

Rebecca Mason

Ron Owens

Yaxiao Song

A 'Facet Recommender' creates conversational recommendations for facets of particular conversational topics, and optionally for things associated with those facets, from consumer reviews or other social media content. The Facet Recommender applies a machine-learned facet model and optional sentiment-model, to identify facets associated with spans or segments of the content and to determine neutral, positive, or negative consumer sentiment associated with those facets and, optionally, things associated with those facets. These facets are selected by the facet model from a list or set of manually defined or machine-learned facets for particular conversational topic types. The Facet Recommender then generates new conversational utterances (i.e., short neutral, positive or negative suggestions) about particular facets based on the sentiments associated with those facets. In various implementations, utterances are fit to one or more predefined conversational frameworks. Further, responses or suggestions provided as utterances may be personalized to individual users.

Sentiment-based recommendations as a function of grounding factors associated with a user

Facet recommendations from sentiment-bearing content

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Redmond, WA, United States of America

Jay Banerjee