3D Preference Space Model for Food Recommendation Accuracy
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Solution Overview
Problem
Existing techniques for suggesting foods based on personal preferences are plagued by low accuracy.
Innovation Solution
A preference estimation method using a preference space model that represents objects and people in a three-dimensional space, where the preference is higher as the distance between the object and person points is shorter, allowing for accurate estimation of preferred objects and people who share those preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional food recommendation systems are used, then food suggestions can be provided, but the accuracy of the suggestions is low
Solution Approach 1:
The patent transforms preference data from traditional flat structures into a three-dimensional preference space model. This dimensional transformation allows for more nuanced representation of preferences by capturing relationships across multiple dimensions simultaneously, thereby improving both estimation accuracy and recommendation reliability without sacrificing system simplicity
2Measurement precision
If simple recommendation systems are used, then implementation is easy, but they cannot accurately estimate person-object preferences
Solution Approach 1:
The invention introduces a three-dimensional preference space model that systematically organizes person preferences and object characteristics. This structured dimensional approach enables accurate preference measurement while maintaining computational tractability through geometric distance calculations, avoiding the need for overly complex algorithms
3Reliability
If detailed preference analysis is performed, then accurate recommendations can be made, but the system becomes more complex
Solution Approach 1:
The patent employs a three-dimensional preference space that systematically captures detailed preference information through geometric relationships. By representing preferences as spatial positions and relationships in 3D space, the system achieves high recommendation reliability through intuitive distance-based calculations rather than complex analytical models
Data Source
AI summary
A preference estimation method includes (1) a model generating step of generating, using preference information as information representing a preference of a person for an object, a preference space model as a model that includes a plurality of points each representing the object and a plurality of points each representing the person in a three-dimensional space and represents that a preference of the person for the object is higher as a distance between the point representing the object and the point representing the person is shorter, and (2) an object estimating step of estimating, using the preference information about a preference estimation person as a person whose preference is estimated, an object for which the preference estimation person has a high preference, based on the preference space model.