Ingredient-Seasoning Compatibility Prediction With Preference-Aware Models
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Solution Overview
Problem
Existing methods for predicting compatibility between ingredients and seasonings fail to consider individual meal preferences, leading to inaccurate predictions.
Innovation Solution
A prediction apparatus and learning method that calculates an index score based on flavor data for ingredients and seasonings, using a compatibility prediction model trained on pre-aggregated evaluations from the food industry, to output a compatibility score considering personal preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compatibility is predicted only based on ingredients and seasonings without considering meal preferences, then the prediction method is simple, but the prediction accuracy is insufficient
Solution Approach 1:
The prediction model is segmented into multiple independent components: ingredient feature extraction, seasoning feature extraction, and preference feature extraction. Each component processes specific aspects separately, then their results are integrated to achieve accurate compatibility prediction while maintaining manageable complexity in each segment.
Solution Approach 2:
The prediction system uses a composite approach by combining multiple types of features (ingredient characteristics, seasoning characteristics, and preference data) into an integrated compatibility score. This composite model leverages the strengths of each feature type to achieve higher prediction accuracy than any single feature type alone.
2Ease of operation
If a compatibility prediction model is trained on pre-aggregated evaluation results, then the model can be applied without specialized knowledge, but the model requires substantial training data
Solution Approach 1:
The system performs preliminary action by pre-training the compatibility prediction model using aggregated evaluation results from multiple sources before deployment. This offline training phase accumulates the necessary data processing, so that when the model is used in practice, it can provide accurate predictions without requiring users to have specialized knowledge or manually aggregate data.
Data Source
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AI summary
Provided is a prediction apparatus including: an input section; a processing section; and an output section. The processing section extracts an index score which is inputted into a compatibility prediction model for an ingredient and a seasoning. The output section outputs a compatibility score as a predicted value of compatibility between the ingredient and the seasoning, where the compatibility score has been outputted from the compatibility prediction model by inputting the index score, which has been extracted by the processing section, into the compatibility prediction model.