ML Model Recommendation via Semantic Relatedness Scoring
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Solution Overview
Problem
Data scientists face challenges in identifying suitable machine learning models and source codes for given datasets due to the vast number of available assets, making it impractical to manually select the appropriate resources for specific tasks, and thus increasing user time and effort.
Innovation Solution
A computer-implemented method that calculates a relatedness score between an input dataset and candidate data analysis assets, ranking and recommending pre-trained machine learning models and source codes based on semantic matching, including schema, user types, problem types, data transformations, and historical usage patterns, to facilitate efficient asset selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data scientists manually search through vast numbers of available machine learning models and source codes to find suitable assets for given datasets, then they can potentially find the most appropriate resources, but the process requires excessive user time and effort
Solution Approach 1:
The system performs self-service by automatically calculating relatedness scores between datasets and candidate assets, generating ranked lists without requiring manual user intervention. The computer autonomously processes the matching task using semantic analysis and historical usage patterns, freeing users from time-consuming manual searches while maintaining high selection accuracy
Solution Approach 2:
The patent replaces the mechanical manual searching process with an automated computational system. Instead of users manually browsing and evaluating assets, the system uses semantic analysis, relatedness score calculations, and algorithmic ranking to automatically identify and present the most suitable machine learning models and source codes
2Measurement precision
If the system provides comprehensive semantic analysis including schema, user types, problem types, data transformations, and historical usage patterns, then the recommendation accuracy improves, but the system complexity increases
Solution Approach 1:
The system segments the complex recommendation task into distinct analytical components: schema matching, user type analysis, problem type classification, data transformation evaluation, and historical usage pattern examination. Each component processes specific aspects of the dataset-asset relationship independently, then combines results to generate the final relatedness score, making the overall system more manageable and maintainable
Solution Approach 2:
The system implements a universal semantic analysis framework that handles multiple types of assets (machine learning models, source codes, notebooks) and multiple analysis dimensions (schema, user types, problem types, transformations, historical patterns) through a unified approach. This multi-functional design improves recommendation accuracy across diverse scenarios while avoiding the need for separate specialized systems for each asset type or analysis dimension
Data Source
AI summary
Asset recommendation for a particular input dataset is provided. Candidate data analysis assets having a corresponding relatedness score associated with the particular input dataset greater than a defined relatedness score threshold value are selected. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are ranked by score. Those candidate data analysis assets having a corresponding relatedness score greater than the defined relatedness score threshold value are listed by rank from highest to lowest. A justification for each candidate data analysis asset is inserted in the ranked list of candidate data analysis assets. The ranked list of candidate data analysis assets along with each respective justification is outputted on a display device.