Demand Prediction Weighting for Similar Products and User Intent
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Solution Overview
Problem
Existing demand prediction techniques fail to adequately consider a user's intention regarding a target product, limiting the accuracy and relevance of demand forecasts.
Innovation Solution
A demand prediction device that integrates multiple individual learning models, each weighted differently, allows for specifying similar products, calculating a base demand quantity, and receiving user operations to adjust weights, thereby refining the demand prediction based on product features and user intent.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple individual learning models are integrated with different weights, then prediction accuracy is improved, but device complexity increases
Solution Approach 1:
The prediction system is segmented into multiple individual learning models, each specializing in different aspects of demand prediction. Each model processes specific features or patterns independently, and their results are combined through weighted integration to achieve higher overall accuracy while maintaining manageable complexity through modular design
Solution Approach 2:
Multiple individual learning models are merged into a unified prediction framework where their outputs are combined through weighted averaging. This merging allows the system to leverage the strengths of different models simultaneously, improving prediction accuracy by capturing diverse patterns in the data
2Adaptability or versatility
If user operations to change weights are allowed, then adaptability to user intention is improved, but ease of operation worsens
Solution Approach 1:
The weight parameters of the individual learning models are made dynamic rather than fixed, allowing users to adjust them according to their intentions and specific prediction scenarios. This dynamic adjustment capability enables the system to adapt to different user needs and priorities while providing flexibility in model configuration
Solution Approach 2:
The system allows users to change the weight parameters of individual learning models to reflect their intentions and priorities. By enabling parameter adjustment, the system becomes more versatile and can be tailored to different prediction scenarios, user preferences, and business requirements
Data Source
AI summary
A demand prediction device is a device predicting a demand quantity for a target product based on a prediction model, and the demand prediction device specifies a similar product to the target product, calculates a base demand quantity for the target product by using the prediction model weighted in accordance with the similar product and a feature of the target product, the prediction model is a model in which two or more types of individual learning models are integrated and a weigh is set for each of the individual learning models, the individual learning model is a model that outputs the demand quantity in accordance with a feature of a product, receives an operation of changing the weight for each of the individual learning models, and calculates a predicted demand quantity for the target product by using the prediction model. The present disclosure can be used to support decision making.