Oath Inc.

Verizon Media Inc.

Sungjin Lee

Amanda Joy Stent

Aasish Kumar Pappu

Joel Tetreault

Yifan Hu

Xiang Li

The present teaching relates to generating user profiles with semantic knowledge. A first information associated with a user is obtained. One or more entities are identified from the first information. The one or more entities are augmented based on second information to generate a set of augmented entities. The set of augmented entities are clustered into a set of hierarchical clusters. A set of user profiles is generated based on the set of hierarchical clusters so that the user profile is to be used to personalize content recommendation.

Method and system for user profiling for content recommendation

A fact machine receives a comment string from a webpage and segments the comments into sub-conversations. The fact machine also identifies entities within comments or draft comments as well as a sentiment and/or attributes associated with the entity. The fact machine then queries a knowledge base for facts relating to the entity, the attributes, and/or the sentiment. The fact machine then provides the facts to a user prior to the user posting a draft comment.

Fact machine for user generated content

The present teaching relates to facilitating a guided dialog with a user. In one example, an input utterance is obtained from the user. One or more task sets are estimated based on the input utterance. Each of the one or more task sets includes a plurality of tasks estimated to be requested by the user via the input utterance and is associated with a confidence score computed based on statistics with respect to the plurality of tasks in the task set. At least one of the one or more task sets is selected based on their respective confidence scores. A response is generated based on the tasks in the selected at least one task set. The response is provided to the user.

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Edgewater, NJ, United States of America

Sungjin Lee