Synthetic Data Generation for Service Recommendation Models
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Solution Overview
Problem
The lack of real training data poses challenges in training, generating, and evaluating service recommendation models, especially due to privacy concerns and the unavailability of authentic data.
Innovation Solution
The system generates synthetic data that maintains statistical properties of real data while protecting sensitive information, using randomized and advanced machine learning techniques, and evaluates this synthetic data to ensure quality and privacy preservation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real training data is used to train service recommendation models, then model performance and effectiveness are improved, but user privacy and data security are compromised
Solution Approach 1:
The patent creates synthetic copies of real training data that replicate the statistical properties and patterns necessary for model training while containing no actual sensitive user information. These synthetic data copies serve as substitutes for real data, allowing models to be trained effectively without exposing private user information.
Solution Approach 2:
The patent introduces synthetic data as an intermediary between the need for training data and the requirement for privacy protection. This intermediary layer allows model training to proceed with data that has the necessary statistical characteristics while eliminating the direct use of sensitive real user data.
2Object-affected harmful factors
If real training data is unavailable due to privacy protection, then data security is maintained, but model training and evaluation become challenging
Solution Approach 1:
The system generates synthetic copies of training data that preserve the essential statistical properties and relationships needed for model training and evaluation. These copies enable comprehensive model development while maintaining data security by containing no real sensitive information.
Solution Approach 2:
The patent transforms real data into synthetic data by changing key parameters while preserving statistical properties. This transformation allows the data to maintain its utility for training purposes while eliminating the privacy risks associated with real user information.
3Object-affected harmful factors
If synthetic data is generated to protect privacy, then user privacy is enhanced, but data quality and statistical accuracy may deteriorate
Solution Approach 1:
The patent carefully controls parameter changes during synthetic data generation to preserve critical statistical properties such as distributions, correlations, and patterns while modifying parameters that contain sensitive information. This selective parameter transformation maintains data accuracy for training purposes while achieving privacy protection.
Solution Approach 2:
The synthetic data generation process creates copies that faithfully replicate the statistical properties and patterns of real data through controlled parameter transformations, ensuring that the copied data maintains the necessary accuracy and relationships for effective model training.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for synthetic data generation for service recommendation models. One of the methods includes generating a plurality of synthetic data items, each synthetic data item including user data, context data, service data of a service, and user action data characterizing one or more user actions with respect to the service; for each synthetic data item of the plurality of synthetic data items, processing the synthetic data item using an evaluator to generate a score indicating quality of the synthetic data item; selecting a subset of synthetic data items from the plurality of synthetic data items; and providing the subset of synthetic data items into a service recommendation model for recommending one or more services to one or more users based on the subset of synthetic data items.