Adaptive ML Model Weighting for Personalized Virtual Networks
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Solution Overview
Problem
Existing data analysis methods require extensive domain expertise and resources, often providing generalized guidance insufficient for personalized settings, and struggle to efficiently apply detailed and individualized data across various scenarios.
Innovation Solution
A system and method for generating a personalized virtual network using adaptive weighting of machine learning models, which involves acquiring user requests and preferences, identifying conditions, determining propensities, and applying weights to machine learning models to create a tailored network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data analysis methods are used to create personalized models, then measurement precision is improved, but loss of time and productivity deteriorate significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user information in structured formats, and by pre-training machine learning models with relevant data. When a service request is received, the system can quickly retrieve pre-processed user data and apply pre-trained models, avoiding the need to create personalized models from scratch for each request, thus significantly reducing the time required while maintaining accuracy
Solution Approach 2:
The system creates simplified copies or representations of user data (such as user profiles, preference vectors, or feature embeddings) that can be quickly processed. Instead of analyzing raw, unstructured data each time, the system uses these pre-computed copies to rapidly generate personalized results, reducing processing time while preserving the essential information needed for accurate personalization
2Measurement precision
If traditional data analysis methods are used to create personalized models, then measurement precision is improved, but device complexity and resource requirements worsen
Solution Approach 1:
The system segments the complex data analysis task into multiple independent machine learning models, each responsible for analyzing specific aspects or features of user data. This modular approach allows the system to maintain high measurement precision by using specialized models for different analysis dimensions, while reducing overall system complexity by breaking down the monolithic analysis process into manageable, independently trainable components
Solution Approach 2:
The system changes parameters by using machine learning models that can dynamically adjust their analysis depth, data processing intensity, and computational resources based on the specific service request and user profile. This allows the system to maintain high accuracy when needed while reducing computational complexity for routine or less critical analyses, optimizing the balance between precision and resource requirements
3Device complexity
If generalized models are used, then device complexity is reduced, but adaptability to personalized settings deteriorates
Solution Approach 1:
The system implements dynamics by making the model selection and parameter configuration adaptive rather than static. When a service request is received, the system dynamically determines which machine learning models to apply and how to configure them based on the specific user information, service type, and contextual factors. This dynamic approach allows the system to maintain simplicity in the underlying model architecture while achieving high adaptability to personalized settings through flexible model组合 and parameter adjustment
Data Source
AI summary
Methods, systems, and computer-readable media for generating a personalized virtual network. The method acquires a request for a service associated with a user and their preferences. The method then identifies one or more conditions of the user and their propensities based on a first set of machine learning models using stored past information of the user. The method next identifies a second set of machine learning models and evaluates the weightage of each model based on the determined propensities of the conditions. The evaluated weights are applied to the second set of models to generate a personalized virtual network for the user.