AI Recommendation System Using Embedding Vectors for Provider Matching
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Solution Overview
Problem
Existing recommendation systems are inefficient and unreliable in accurately matching entities with optimal selections in high-dimensional categorical feature spaces, particularly in healthcare where provider recommendations need to consider diverse and complex input spaces.
Innovation Solution
A machine learning-based recommendation system that generates embeddings data to encode semantic relations among latent features for provider entities, using techniques like graph embedding and factorization machines to predict and rank provider entities based on user data, ensuring accurate and efficient recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional recommendation systems are used to match entities in high-dimensional categorical feature spaces, then the system structure is simple, but the accuracy and reliability of recommendations deteriorate
Solution Approach 1:
The patent introduces embedding vectors as an intermediary representation that transforms high-dimensional categorical features into continuous vector space. These embeddings serve as mediators between the input features and the recommendation model, enabling more accurate matching while maintaining computational efficiency. The embeddings capture semantic relationships and latent patterns that conventional systems miss.
Solution Approach 2:
The patent transforms the parameter representation from discrete categorical values to continuous embedding vectors. This parameter change allows the system to capture nuanced relationships and patterns in high-dimensional spaces, significantly improving recommendation accuracy. The transformation maintains the essential information while enabling more sophisticated processing.
2Measurement precision
If complex machine learning models are used to process high-dimensional categorical features, then recommendation accuracy improves, but computational efficiency deteriorates
Solution Approach 1:
The patent performs preliminary processing by pre-computing embedding vectors for categorical features before the main recommendation process. This preliminary action transforms complex categorical data into efficient vector representations, reducing the computational burden during actual recommendation generation while maintaining high accuracy.
Solution Approach 2:
The patent replaces conventional mechanical processing of categorical features with neural network-based embedding layers. This substitution enables automatic learning of feature relationships and patterns, achieving superior accuracy with improved computational efficiency compared to traditional rule-based or statistical methods.
3Adaptability or versatility
If existing recommendation systems process high-dimensional categorical feature spaces, then the feature space coverage is limited, but the system complexity remains low
Solution Approach 1:
The patent implements a universal embedding framework that can handle diverse categorical features across different domains and applications. The same embedding mechanism works for various feature types (user attributes, item attributes, contextual features), providing versatile feature space handling capability while maintaining a cohesive system architecture.
Solution Approach 2:
The patent transitions from handling high-dimensional categorical features directly to representing them in a lower-dimensional continuous vector space. This dimensional transformation preserves the essential information and relationships while enabling the system to process and adapt to diverse feature spaces efficiently, greatly enhancing versatility.
Data Source
AI summary
Various embodiments of the present disclosure facilitate recommendation prediction using machine learning. In one example, an embodiment provides for generating embeddings data related to one or more provider entities, predicting a set of provider entities for a patient entity based on a provider machine learning model, ranking provider entities in the set of provider entities to generate a ranked set of provider entities, and performing one or more actions to provide a recommendation for the patient entity based on the ranked set of provider entities.