Hypergraph Recommendation System for Overfitting Reduction
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Solution Overview
Problem
Conventional product recommendation systems face issues with continuous parameter updating and overfitting due to the lack of negative feedback from consumers, leading to poor discrimination between relevant and irrelevant recommendations.
Innovation Solution
A hypergraph-based recommendation system that generates consumer and product vectors using an incidence matrix, eliminating the need for continuous parameter updating and negative values, by employing graph neural networks and hypergraph spectral learning to score affinities between consumers and products.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems continuously update parameters to improve recommendation accuracy, then recommendation precision may improve, but the system becomes prone to overfitting and requires negative feedback that consumers do not provide
Solution Approach 1:
The patent extracts and removes the problematic continuous parameter updating mechanism and negative feedback requirement from the recommendation system. By using a two-tower model with static embedding vectors that are updated only during training phases rather than continuously, the system eliminates the source of overfitting while maintaining recommendation capability.
Solution Approach 2:
The patent performs preliminary action by pre-training embedding vectors for consumers and products using available positive feedback data before deployment. These pre-computed embedding vectors are then used for scoring during inference without requiring continuous updates, thus preventing overfitting while maintaining accuracy.
2Productivity
If conventional systems use Laplacian matrix tables and continuous parameter updating to improve recommendations, then they can process consumer data, but they suffer from overfitting and poor discrimination between relevant and irrelevant recommendations
Solution Approach 1:
The patent substitutes the mechanical Laplacian matrix table approach with a neural network-based two-tower model. This replacement uses embedding vectors and affinity scoring that naturally provides better discrimination between relevant and irrelevant recommendations without the overfitting issues of continuous parameter updating.
Solution Approach 2:
The patent changes the parameter representation from continuous updating of Laplacian matrix parameters to static embedding vectors that are computed once during training. This parameter change fundamentally alters how the system processes data, maintaining productivity while improving discrimination through the nature of embedding-based similarity scoring.
3Reliability
If recommendation systems rely on consumer negative feedback to prevent overfitting, then model generalization may improve, but the system fails when such feedback is unavailable
Solution Approach 1:
The patent implements self-service by designing a system that uses only positive feedback (purchases) to train the embedding vectors. The two-tower model inherently prevents overfitting through its architecture and training objective, eliminating the need for external negative feedback while maintaining generalization capability and robustness.
Data Source
AI summary
Aspects of the invention include a computer-implemented method for generating product recommendations used a hypergraph. The computer-implemented method includes generating a product embedding vector for a product unpurchased by a consumer based on a hypergraph. Generating a consumer embedding vector for the consumer based on the hypergraph. An affinity between the consumer and a product unpurchased by the consumer is scored based on the product embedding vector and the consumer embedding vector. A digital description of the unpurchased product is retrieved based on the scoring. The digital description is presented to the consumer.