Graph Neural Network for Data Recommendation
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Solution Overview
Problem
Conventional systems for recommending data attributes and visualizations in large digital data repositories face challenges with flexibility, data sparsity, and accuracy, particularly due to rigid rule-based approaches, inability to model users, and issues with disjoint datasets and data sparsity.
Innovation Solution
A graph neural network framework is used to generate personalized data recommendations by formulating a digital graph representation of users, data attributes, and visualizations, mapping attributes to a shared meta-feature space, and generating user-specific embeddings to predict relevant attributes and visualizations for client devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based approaches are used for recommending data attributes and visualizations, then the system is simple to implement, but the system lacks flexibility and accuracy in capturing user preferences and data interactions
Solution Approach 1:
The patent replaces rule-based mechanical recommendation systems with a graph neural network-based intelligent system. The GNN automatically learns complex patterns and relationships from data interactions, eliminating the need for manual rule configuration while providing adaptive, flexible recommendations that capture user preferences and data attribute relationships.
Solution Approach 2:
The system transforms the recommendation approach from static rule-based parameters to dynamic learned parameters through graph neural network training. The GNN learns optimal recommendation parameters from interaction data, allowing the system to adapt to changing user preferences and data characteristics without manual reconfiguration.
2Ease of manufacture
If rule-based approaches are used for recommending data attributes and visualizations, then the system is simple to implement, but the accuracy of recommendations deteriorates due to inability to model users and capture complex interactions
Solution Approach 1:
The patent replaces rule-based mechanical recommendation systems with a graph neural network-based intelligent system. The GNN automatically learns complex patterns and relationships from data interactions, eliminating the need for manual rule configuration while providing adaptive, flexible recommendations that capture user preferences and data attribute relationships.
Solution Approach 2:
The patent introduces a graph neural network as an intermediary between raw interaction data and recommendation outputs. The GNN processes user interactions with data attributes and visualizations through learned graph representations, enabling accurate capture of complex relationships and user preferences that direct rule-based systems cannot model.
3Reliability
If conventional systems process disjoint datasets separately, then data privacy and security are maintained, but data sparsity issues worsen and recommendation accuracy deteriorates
Solution Approach 1:
The patent creates a universal graph neural network model that can process multiple disjoint datasets through a common framework. The GNN learns from interactions across different datasets while maintaining their individual characteristics, enabling the system to overcome data sparsity in individual datasets by leveraging patterns from multiple datasets without compromising security or privacy.
Solution Approach 2:
The patent segments the processing approach by creating separate graph representations for different datasets while using a unified GNN model. This allows the system to process each dataset independently for security reasons while still learning from cross-dataset patterns through the shared neural network architecture, effectively addressing data sparsity without compromising data isolation.
4Adaptability or versatility
If graph neural network framework is used to generate personalized recommendations, then flexibility and accuracy improve, but device complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training the graph neural network model and pre-computing graph representations of datasets and user interactions. This allows the complex GNN processing to be done offline in advance, reducing the complexity of real-time recommendation generation while maintaining the flexibility and accuracy benefits of the GNN approach.
Solution Approach 2:
The patent implements self-service mechanisms where the graph neural network automatically learns and adapts to user preferences and data relationships without manual intervention. The system performs self-training, self-optimization, and automatic parameter tuning, reducing the operational complexity despite the increased structural complexity of the GNN architecture.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media that utilize a graph neural network to generate data recommendations. The disclosed systems generate a digital graph representation comprising user nodes corresponding to users, data attribute nodes corresponding to data attributes, and edges reflecting historical interactions between the users and the data attributes; Moreover, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. In addition, the disclosed systems generate, utilizing a graph neural network, user embeddings for the user nodes and data attribute embeddings for the data attribute nodes from the digital graph representation. Furthermore, the disclosed systems determine a data recommendation for a target user utilizing the data attribute embeddings and a target user embedding corresponding to the target user from the user embeddings.