Personality-Typed Machine Learning Model for Personalized Guidelines
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Solution Overview
Problem
Current digital assistants struggle to provide personalized activity suggestions for users due to the scarcity of initial user data and the challenge of sharing feedback across users with different personalities, leading to inferior results.
Innovation Solution
A method and system that determine a user's personality type and select a corresponding machine learning model from a pool of personality-typed models, allowing for the generation of personalized recommendations. This system collects and reweighs feedback data across users using a collaborative personality-based feedback harmonizer to update individual models, ensuring personalized guidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If feedback data is shared across users to overcome data scarcity, then the availability of training data is improved, but the personalization accuracy deteriorates because users have different personalities
Solution Approach 1:
The patent segments users into different personality types (e.g., using Big Five personality dimensions) and creates separate machine learning models for each personality type. This segmentation allows feedback data to be shared within personality type groups while maintaining personalization accuracy by ensuring data is shared with similar users rather than all users indiscriminately.
Solution Approach 2:
The patent applies local quality by tailoring the machine learning model to each user's specific personality type. Instead of using a single universal model or sharing data across all users, the system adapts the model parameters and data selection based on the local characteristics of each personality type, ensuring that data sharing occurs within appropriate boundaries.
2Device complexity
If a single machine learning model is used for all users, then the system complexity is reduced, but the personalization quality deteriorates
Solution Approach 1:
The patent creates a universal framework that handles multiple personality types through a common architecture. The system uses a standardized process for personality assessment, model selection, and feedback integration that works across all user types, providing multi-functionality without requiring completely separate systems for each personality type.
Solution Approach 2:
The patent changes parameters of the machine learning model based on user personality type. Instead of using fundamentally different models, the system adjusts model parameters, hyperparameters, and data weighting based on the detected personality type, allowing a single model framework to adapt to different user characteristics.
3Measurement precision
If personalized models are created for each user from scratch, then the personalization accuracy is improved, but the training time and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-training machine learning models for each personality type using aggregated feedback data from all users of that personality type. When a new user is encountered, the system selects the pre-trained model corresponding to the user's personality type, providing immediate personalization without requiring extensive training time from scratch.
Solution Approach 2:
The system enables self-service by allowing the pre-trained personality-specific models to automatically adapt to individual users through continued learning from their feedback. The models serve themselves by continuously updating with user-specific data while maintaining the personality-type framework, reducing the need for manual intervention and extensive initial training.
Data Source
AI summary
A method of providing personalized guideline information for a user in a predetermined domain, in which a set of personality types is defined for users of said predetermined domain, includes: determining, by a personality type recognizer, a personality type for a user in order to assign the personality type to said user, selecting a personality-typed machine learning model from a model pool of personality-typed machine learning models based on the personality type of said user, where the selected personality-typed machine learning model is used to initialize an individual personalized machine learning model of said user, and generating, by the individual personalized machine learning model of said user, a recommendation prediction, the recommendation prediction is presented as a guideline information to said user.