Personalized UX Variant Selection vs Global-Best Assignment
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Solution Overview
Problem
Conventional user experience research methods adopt a one-size-fits-all approach, selecting a global-best user experience variant without considering individual user preferences, which can negatively impact user engagement.
Innovation Solution
Implement a user personalization option using machine learning to predict and serve user-preferred experience variants based on user data, optimizing engagement through personalized experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a global-best user experience variant is selected for all users, then implementation simplicity is improved, but user engagement deteriorates due to lack of personalization
Solution Approach 1:
The patent applies local quality by transitioning from a uniform global variant assignment to personalized variant selection based on individual user characteristics. The system analyzes user-specific data (demographics, behavior patterns, preferences) to determine which experience variant is optimal for each user, thereby implementing local customization rather than uniform treatment. This resolves the contradiction by maintaining implementation feasibility through automated algorithms while significantly improving user engagement through personalized experiences.
Solution Approach 2:
The patent implements dynamics by making the variant assignment flexible and adaptive rather than static. The system continuously learns from user interactions and updates personalized recommendations, allowing the user experience to dynamically adjust to individual user needs and preferences. This dynamic approach enables the system to improve user engagement over time while managing complexity through automated machine learning models that adapt to changing user behaviors.
2Productivity
If user personalization is implemented, then user engagement is improved, but system complexity increases due to machine learning model requirements
Solution Approach 1:
The patent applies self-service by implementing automated machine learning models that autonomously analyze user data, predict preferred experience variants, and make assignment decisions without extensive manual intervention. The system self-trains on user interaction data and continuously optimizes personalized recommendations, reducing the need for complex manual configuration and management. This enables the system to handle personalization complexity internally while delivering improved user engagement.
Solution Approach 2:
The patent utilizes parameter changes by transforming user characteristics (demographics, behavior patterns, preferences) into predictive parameters that drive variant selection. The machine learning model processes multiple user parameters and their interactions to determine optimal experience variants. This approach manages system complexity by structuring personalization around defined parameters and their relationships, enabling scalable implementation while maintaining high user engagement through personalized recommendations.
3Productivity
If individualized experience variants are provided, then user engagement is improved, but data processing requirements increase
Solution Approach 1:
The patent applies taking out by extracting and focusing on the most predictive user characteristics and behavior patterns from large datasets. Rather than processing all available user data equally, the system identifies and extracts key features (demographics, primary behavior patterns, strong preferences) that most significantly influence experience variant preference. This extraction approach reduces data processing requirements while maintaining high user engagement by concentrating computational resources on the most impactful personalization factors.
Solution Approach 2:
The patent implements partial action by providing personalized experience variants for the most influential user characteristics while using more general assignment strategies for less impactful factors. The machine learning model focuses computational resources on predicting variants based on high-weight user parameters, applying full personalization where it matters most and using simplified approaches elsewhere. This partial personalization strategy improves user engagement through targeted customization while reducing overall data processing requirements compared to comprehensive personalization of all user attributes.
Data Source
AI summary
Techniques for evaluating a user experience experiment designed to use one user experience variant selected from a number of user experience variants as a global-best user experience variant to be used across users relative to a machine model trained to use user data to identify a user-preferred user experience variant. Disclosed systems and methods provide techniques for optimizing user response. In one embodiment, a global-best user experience variant is evaluated by comparing an aggregate user response determined for the global-best user experience variant to an aggregate user response determined using user response predictions determined using the trained machine model, and using the outcome of the comparison to make a recommendation as to which one of the global-best user experience variant and the trained machine model to adopt for providing a user experience to users.