Groupon, Inc.

Ana Ananthakumar

Andrew Lisy

Hechao Sun

Situo Liu

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

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.

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Chicago, IL, United States of America

Ana Ananthakumar