Intuit, Inc.

Nan Jiang

Grace Wu

Suman Sundaresh

Zachary Dorsch

Shahram Mohrehkesh

Mustapha Harb

Dawn-Marie Chantel Miesner

Yashwanth Musiboyina

Zhao Hu

Aspects of the present disclosure provide techniques for dynamic reward-driven intervention in a software application. Embodiments include determining rewards associated with user actions in the software application using a reward machine learning model trained based on prior instances of the user actions associated with values for a target attribute, each of the prior instances of the user actions being performed within a particular time interval following a prior intervention provided via the software application. Embodiments include providing interventions to a user via the software application. Embodiments include detecting that the user has performed one or more respective user actions of the user actions after the providing of each intervention of the interventions. Embodiments include training an intervention machine learning model based on the detecting and the rewards. Embodiments include providing a targeted intervention to the user via the software application based on the training of the intervention machine learning model.

Targeted interventions via long term reward attribution in complex software ecosystems

The instant systems and methods are directed to a contextual bandits machine learning model configured to enable granular synchronized ecosystem personalization and optimization. The system and methods determine an objective and feed the objective and one more lifecycle model propensity scores as inputs to the contextual bandits machine learning model. The contextual bandits machine learning model then generates one or more potential weighted model rewards, wherein each potential weighted model reward includes at least a desired user action, a weight, a channel, and an expected change to the objective, and selects a weighted model reward that optimizes the objective. An action recommendation is subsequently transmitted to a user device based on the weighted model reward, wherein the action recommendation is presented in a selected channel associated with the weighted model reward. Feedback associated with the action recommendation is collected and used in training and fine-tuning of the model.

Contextual bandits-based ecosystem recommender system for synchronized personalization

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Mountain View, CA, United States of America

Nan Jiang