Microsoft Technology Licensing, LLC

Xuetao Yin

Kristina Nikolova Toutanova

Pallavi Choudhury

Margaret Ann Mitchell

Susan Michele Hendrich

Mouni Reddy

Jay Banerjee

Rebecca Mason

Ron Owens

Yaxiao Song

Liang Xu

Bill Dolan

Houdong Hu

Julia Gong

Jyotkumar Jagdishbhai Patel

Xiujia Guo

Rajiv S Binwade

Yale Song

The present disclosure provides method and apparatus for determining a food item from a photograph and a corresponding restaurant serving the food item. An image is received from a user, the image being associated with a consumable item. One or more ingredients of the consumable item in the image is identified along with a location of the user and using a neural network, determining one or more similar images from a database. A restaurant associated with each of the one or more similar images is determined along with a similarity score indicating a similarity between the restaurant and the identified content of the image. The one or more restaurants and/or associated similar food items are ranked based on the similarity score and a list of ranked restaurants is provided to the user.

Providing local recommendations based on images of consumable items

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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Xuetao Yin