Shared Learning Model for Product Attribute Prediction Load Reduction
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Solution Overview
Problem
Existing e-commerce systems face a significant processing load due to the large number of products and attributes, making it inefficient to predict various attributes related to products.
Innovation Solution
An information processing apparatus and method using a shared learning model with multiple estimation layers to predict attributes, where the model includes an input layer for feature extraction and an output layer for concatenating attribute values, reducing processing load by predicting only valid attributes based on the product type.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If preset attributes are stored for all products, then attribute information completeness is improved, but processing load increases
Solution Approach 1:
The patent replaces the mechanical approach of storing preset attributes with an image-based prediction system using deep learning models. Instead of manually storing and managing attribute data for each product, the system uses neural networks to automatically predict attributes from product images, thereby reducing processing load while maintaining information completeness.
Solution Approach 2:
The patent changes the fundamental parameter of attribute representation from stored data to predicted data. By transforming the system from storing predefined attributes to dynamically predicting attributes based on image analysis, the processing load is reduced while maintaining comprehensive attribute information through the prediction model.
2Adaptability or versatility
If multiple attributes are predicted for each product, then attribute coverage is improved, but prediction complexity increases
Solution Approach 1:
The patent segments the attribute prediction process into multiple specialized estimation layers, where each layer is responsible for predicting specific types of attributes. This segmentation allows the system to handle diverse attribute coverage while managing complexity by dividing the prediction task into modular, specialized components rather than a single monolithic prediction system.
Solution Approach 2:
The patent creates a universal prediction model that can handle multiple attribute types across different product categories. The deep learning model is designed to be multi-functional, capable of predicting various attributes (color, size, material, etc.) for different product types using a unified architecture, thereby achieving broad attribute coverage without proportionally increasing overall system complexity.
Data Source
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Figure 2B~3A
Figure 3B
AI summary
An information processing apparatus (1) includes: an acquisition means (101) for acquiring an object image including an object; and a prediction means (102) for predicting various attributes relating to the object by applying a learning model to the object image acquired by the acquisition means, wherein the learning model is a learning model that is shared between a plurality of different objects including the object and includes a plurality of estimation layers that estimate a plurality of attribute values for a plurality of attributes relating to the plurality of different objects.