Groupon, Inc.

Bytedance Inc.

Jonathan Esterhazy

Gaston L'Huillier

Hernan Enrique Arroyo Garcia

Francisco Jose Larrain

Dor Levi

Amit Koren

Daniel Langdon

Bhupesh Bansal

Feras Karablieh

Srinivasa Raghavan Vedanarayanan

Shawn Ryan Jeffery

Juzheng Li

David Alan Johnston

Shawn Ryan Jeffrey

Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically analyzing a consumer review. The method includes programmatically accessing, via a network device, one or more consumer reviews for a commercial entity or a commercial object. The method also includes executing a consumer review processing engine to programmatically identify an attribute descriptor in the one or more consumer reviews, and executing the consumer review processing engine to programmatically generate a sentiment score associated with the one or more consumer reviews. The method further includes storing, on a non-transitory computer-readable storage device, the attribute descriptor and the sentiment score in association with the commercial entity or the commercial object.

Method and system for programmatic analysis of consumer reviews

Embodiments provide a computer-executable method, computer system and non-transitory computer-readable medium for programmatically processing text information to generate attribute descriptors contained therein. The method includes programmatically accessing, via a network device, a textual unit, the textual unit associated with a commercial entity or a commercial object. The method also includes executing a text processing engine to programmatically generate an attribute descriptor from the textual unit. The method further includes storing, on a non-transitory computer-readable storage device, the attribute descriptor in association with identification of the commercial entity or the commercial object.

System and method for programmatic generation of attribute descriptors

In general, embodiments of the present invention provide systems, methods and computer readable media configured to receive configuration data describing a desired data set distribution, and, in response to receiving new data instances, use the configuration data and the new data instances to dynamically optimize the distribution of data already stored in a data reservoir that has been discretized into bins representing the desired data distribution.

Dynamically optimizing a data set distribution

In general, embodiments of the present invention provide systems, methods and computer readable media for an adaptive oracle-trained learning framework for automatically building and maintaining models that are developed using machine learning algorithms. In embodiments, the framework leverages at least one oracle (e.g., a crowd) for automatic generation of high-quality training data to use in deriving a model. Once a model is trained, the framework monitors the performance of the model and, in embodiments, leverages active learning and the oracle to generate feedback about the changing data for modifying training data sets while maintaining data quality to enable incremental adaptation of the model.

Processing dynamic data within an adaptive oracle-trained learning system using curated training data for incremental re-training of a predictive model

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San Francisco, CA, United States of America

Jonathan Esterhazy