Reward Attribution for Targeted Interventions in Software Ecosystems
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Solution Overview
Problem
Existing automated intervention techniques in software applications require extensive user-specific training data to account for unique user preferences and behavior patterns, which is challenging, time-consuming, and resource-intensive, especially in complex ecosystems where the connection between interventions and long-term target attributes is unclear.
Innovation Solution
A reward-driven machine learning process that assigns rewards to intermediate actions based on learned connections between these actions and target attributes, using a reward model to train an intervention model with proxy rewards, allowing efficient training data generation for individual users without requiring long-term target attribute values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive user-specific training data including ground truth for long-term target attributes is collected to train machine learning models for automated interventions, then the accuracy of intervention predictions is improved, but the training time and resource consumption increase significantly
Solution Approach 1:
The system performs preliminary actions by collecting and storing user interaction data, intermediate actions, and target attribute values in advance. This pre-collected data serves as training data for machine learning models, eliminating the need for time-consuming data collection during the intervention process itself. The reward model is trained beforehand using this pre-assembled training data, enabling fast inference without sacrificing prediction accuracy.
Solution Approach 2:
The patent introduces intermediate actions as mediator variables that bridge the gap between interventions and long-term target attributes. Instead of directly measuring the difficult-to-obtain long-term outcomes, the system uses intermediate actions (shorter-term user behaviors) as proxy indicators. These intermediaries make the training process more efficient while maintaining predictive accuracy through the reward model that learns the relationship between intermediate actions and final target attributes.
2Adaptability or versatility
If ground truth data for long-term target attributes is collected for each individual user to train personalized intervention models, then the personalization accuracy is improved, but the data collection complexity and resource requirements increase
Solution Approach 1:
The system applies partial action by not requiring complete ground truth data for all users and all time periods. Instead, it uses available interaction data and intermediate actions that partially indicate user responses. The reward model learns from this partial information and can generalize to predict outcomes for users with limited data, reducing the complexity of comprehensive data collection while maintaining personalization capabilities.
Solution Approach 2:
The patent creates a universal training framework that can handle multiple users and various types of interactions through a single reward model. The model is trained on aggregated data from multiple users and can be applied universally to generate personalized interventions. This multi-functional approach eliminates the need for separate complex data collection systems for each user, as the same framework handles diverse user behaviors and preferences.
3Measurement precision
If the connection between interventions and long-term target attributes is directly measured in complex software ecosystems, then the attribution accuracy is improved, but the measurement difficulty and time required increase
Solution Approach 1:
The system uses intermediate actions as mediator variables to indirectly measure the connection between interventions and long-term target attributes. Instead of directly attributing changes in difficult-to-measure long-term outcomes to specific interventions, the model observes intermediate user actions that occur after interventions and use these as proxies. The reward model learns the probabilistic relationship between interventions, intermediate actions, and final outcomes, making attribution feasible in complex ecosystems where direct measurement is impractical.
Solution Approach 2:
The patent replaces the mechanical/direct measurement system with a machine learning-based inference system. Instead of directly tracking and measuring the causal relationship between interventions and long-term attributes through complex instrumentation, the system uses the reward model to infer relationships from observed data patterns. This substitution transforms an intractable direct measurement problem into a manageable statistical learning problem.
Data Source
AI summary
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.