Categorization Application Training via Supply Demand Data Fusion
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Solution Overview
Problem
Current listing categorization systems in networked systems face challenges in accurately suggesting categories for new listings, as they rely on manual user input and lack adaptive learning mechanisms to improve categorization accuracy over time.
Innovation Solution
A method and system that utilize a categorization application trained with a set of generated training data from both supply and demand data, applying a classifier to build listing statistics and suggest categories, with adaptive adjustments based on user feedback and repeated rejections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual user input is used for categorization, then the system is simple to implement, but categorization accuracy is insufficient
Solution Approach 1:
The system performs preliminary actions by collecting supply data and demand data beforehand, generating training data sets that are processed offline to create refined training data. This preliminary processing enables the categorization application to learn from historical patterns before actual categorization tasks, improving accuracy without adding complexity to real-time operations
Solution Approach 2:
The system implements feedback mechanisms by using demand data (buyer activity) to refine training data sets. The categorization application continuously learns from the feedback loop where categorization suggestions are made, user responses are collected, and training data is refined based on actual user behavior patterns, progressively improving categorization accuracy
2Measurement precision
If adaptive learning mechanisms are added to improve categorization accuracy, then categorization precision improves, but system complexity increases
Solution Approach 1:
The categorization application performs self-service by automatically training itself using the refined training data set. The system autonomously processes supply data and demand data to generate and refine training data, then uses this refined data to improve its own categorization capabilities without requiring external intervention or complex manual configuration
Solution Approach 2:
The system merges supply data (seller activity) and demand data (buyer activity) into a unified training data generation process. By combining these two data sources and processing them through the same refinement pipeline, the system creates a comprehensive training set that captures both listing characteristics and user preferences, improving categorization accuracy while maintaining a unified system architecture
3Measurement precision
If user feedback is collected and processed, then categorization relevance improves, but processing time increases
Solution Approach 1:
The system performs preliminary processing of user feedback by collecting demand data and refining training data in advance. This offline processing of user responses allows the system to prepare improved training data sets before actual categorization tasks, reducing the time required for real-time categorization while maintaining high relevance
Solution Approach 2:
The system implements periodic action by continuously refining the training data set at scheduled intervals rather than processing every user feedback in real-time. The categorization application periodically retrains using refined training data, balancing the need for up-to-date categorization relevance with acceptable processing time for operational tasks
Data Source
AI summary
Method and system for training a categorization application are provided. An example method comprises applying a classifier to listing data, generating a set of training data for the category structure based on the applying of the classifier to the listing data, and training the categorization application with the set of newly generated training data. The plurality of listings are from at least one of supply data or demand data.