Groupon, Inc.

Bytedance Inc.

Andrew Lisy

Becky Nixon Center

Yingchu Zheng

Eric Hanson

Jae Young Chung

Lingyong Wang

Daniel David Kirsche

Ana Ananthakumar

Derrick C Spell

Bahador Nooraei Beidokht

Situo Liu

Hechao Sun

Xiaohan Zeng

Afsin Bulbul

Al Afsin Bulbul

Methods, apparatus, systems, and computer program products are disclosed for utilizing specially configured machine learning models to generate incremental currency value(s) associated with one or more target merchant data objects. Some embodiments, based on one or more market record sets, identify an actual electronic currency value for a total merchant data object set, and include a counterfactual model configured to generate a counterfactual electronic currency value for use in determining a counterfactual incremental electronic currency impact, and in some embodiments for ranking other models. Embodiments, additionally or alternatively, include an incrementality-trained ensemble model for generating a predictive incremental electronic currency impact. The incrementality-trained ensemble model may be trained to predict based on the rankings of the outputs of the counterfactual model. Embodiments may further rank target merchant data objects and perform one or more additional actions, including assigning the target merchant data objects to sales account data structures for management.

Merchant incremental electronic impact value prediction and ranking using multiple machine learning models

A method, apparatus, and computer program, each configured for determining a travel propensity, the travel propensity being a distance a customer is willing to travel to redeem a renderable data object, are provided An exemplary method comprises classifying each of a plurality of geographic regions as one geographic region type of a plurality of geographic region types, mapping each of a plurality of redemption locations to a geographic region type, calculating, for a first geographic region type, a first distance that accounts for a predefined number of redemptions, determining a number of merchants having redemption locations within the first distance, calculating a second distance, from a center of a geographic region of the second geographic region type, necessary to include the number of merchants having redemption locations within the first distance, and assigning the second distance as the second geographic region type travel propensity.

Methods and systems for determining a travel propensity configured for use in the generation of a supply index indicative of a quality of available supply

Computing systems, computing apparatuses, computing methods, and computer program products are disclosed for machine learning model lifecycle management. An example computing method includes receiving a machine learning model selection, a machine learning model experiment creation input, a machine learning model experiment run type, and a machine learning model input data path. The example method further includes determining a machine learning model execution engine based on the machine learning model experiment creation input and the machine learning model experiment run type. The example method further includes retrieving input data based on the machine learning model input data path. The example method further includes executing a machine learning model experiment based on the machine learning model execution engine, machine learning model experiment creation input, and the input data. The example method further includes generating one or more machine learning model scores based on the machine learning model experiment.

Method, apparatus, and computer program product for machine learning model lifecycle management

Method, apparatus and computer program product for performing a cross-model predictive inference to generate a cross-model predictive output for a plurality of predictive inputs using a plurality of predictive models. For example, the apparatus includes at least one processor and at least one non-transitory memory including program code. The at least one non-transitory memory and the program code are configured to, with the at least one processor, obtain a model selection probability distribution which defines, for each predictive model, a respective selection probability score; obtain, for each predictive model, respective cross-model normalization data; for each predictive input, determine a cross-model predictive score; and determine, based on each determined cross-model predictive score, the cross-model predictive output.

Apparatus, method, and non-transitory computer storage medium for performing predictive inferences using multiple predictive models

A method, apparatus, and computer program, each configured for gauging the health or quality of available supply, are provided. An exemplary method for generating a supply index indicative of a quality or health of supply by calculating and aggregating a supply metric calculated from a perspective of each of a plurality of individual customers to identify those locations where an increase in supply would most positively impact value to customers comprises identifying a location of a user, identifying at least a portion of each of one or more renderable data objects available to a user located at the location, applying a first weight to each of the portion of each of the one or more renderable data objects, calculating a total of each of the weights, applying a coverage cap to determine a capped total, and generating a supply index comprised of aggregated capped totals.

Methods and systems for generating a supply index indicative of a quality of available supply of merchant promotions

Growing community of inventors

River Forest, IL, United States of America

Andrew Lisy