Diet Recommendation Model Using Hierarchical Graph and Attention Mechanism
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Solution Overview
Problem
Existing diet recommendation methods lack accuracy in suggesting recipes that align with users' preferences and nutritional needs, often relying solely on historical dining data without considering broader user preferences.
Innovation Solution
A diet recommendation method that utilizes a recommendation model to determine a target recommended recipe based on historical dining data and multiple candidate foods, employing a hierarchical graph model and random walk algorithm, as well as an attention mechanism model for enhanced accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional diet recommendation methods use only historical dining data, then the system is simple to implement, but the recommendation accuracy is insufficient
Solution Approach 1:
The patent segments the recommendation system into multiple independent modules: user profile analysis module, historical dining data analysis module, nutritional requirement calculation module, and recommendation generation module. Each module processes specific aspects of the data independently, improving accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The patent adds new dimensions to the recommendation system by incorporating not only historical dining data but also user profile information (age, gender, occupation), nutritional requirements, and food preference weights. This multi-dimensional approach significantly improves recommendation accuracy by considering factors beyond simple dining history.
2Measurement precision
If the recommendation model considers multiple user factors and data dimensions, then recommendation accuracy improves, but computational complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing user profiles, nutritional requirements, and food preference weights before the actual recommendation process. This preprocessing step organizes data in advance, reducing the computational energy required during real-time recommendation generation while maintaining high accuracy.
Solution Approach 2:
The system implements self-service mechanisms by automatically updating user profiles and nutritional requirements based on new dining data without requiring manual intervention. The model continuously learns from user feedback and adjusts recommendations autonomously, reducing the energy cost of manual data management while improving accuracy over time.
Data Source
AI summary
A diet recommendation method, device, storage medium and electronic device are described. The diet recommendation method includes: obtaining historical dining data; and determining a target recommended recipe by using a recommendation model based on multiple candidate foods and the historical dining data.