CatBoost Bidding Configuration for Favorable Purchase Prices
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Solution Overview
Problem
Existing bidding systems struggle to optimize bidding configuration parameters, leading to purchases or auctions being completed at unfavorable prices due to numerous and often non-optimized parameters.
Innovation Solution
A computer-implemented bidding method using a CatBoost regression model trained on historical bidding data to optimize bidding configuration parameters, including basic and optimizable parameters, to determine favorable bidding rules for participants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If bidding configuration parameters are manually set without optimization, then the bidding process is simple to operate, but the final price is unfavorable and far from expected targets
Solution Approach 1:
The system performs preliminary actions by training the CatBoost regression model on historical bidding data before actual bidding occurs. The model pre-calculates optimal bidding configuration parameters based on patterns learned from past data, enabling participants to input basic parameters and receive optimized recommendations without manual configuration of all parameters.
Solution Approach 2:
The patent replaces manual mechanical adjustment of bidding parameters with an automated machine learning system. The CatBoost regression model automatically determines optimal parameter values based on historical data patterns, substituting human judgment and manual configuration with algorithmic optimization that achieves more precise price outcomes.
2Measurement precision
If all bidding configuration parameters are optimized manually, then the final price can be improved, but the complexity of parameter configuration increases significantly
Solution Approach 1:
The system implements self-service by enabling participants to input only basic bidding parameters (such as commodity information and budget constraints), while the CatBoost model automatically generates optimized bidding configuration parameters. This self-service approach eliminates the need for users to manually configure complex parameters while still achieving optimized bidding outcomes.
Solution Approach 2:
The patent transforms the bidding system by changing parameters from static manual inputs to dynamic optimized outputs. The CatBoost regression model processes basic input parameters and generates optimized bidding configuration parameters, fundamentally changing how parameters are determined—from user-defined to system-optimized based on historical data patterns.
3Productivity
If bidding parameters are not optimized, then the bidding process is quick and simple, but transactions are completed at unfavorable prices
Solution Approach 1:
The system performs preliminary optimization by pre-training the CatBoost model on historical bidding data before actual bidding transactions. This preliminary action enables the system to quickly generate optimized parameter recommendations during actual bidding without sacrificing speed, as the heavy computational work of pattern recognition has already been completed during model training.
Data Source
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AI summary
The present invention provides a computer-implemented bidding method, computer equipment and a storage medium. In a specific implementation, the method includes: training a CatBoost regression model through a historical bidding data set, where the historical bidding data set includes bidding configuration parameters as an input of the model and a difference between a first quote and a final quote as an output of the model; and inputting current basic bidding parameters into the trained CatBoost regression model, and outputting values of optimized bidding configuration parameters to configure bidding rules for bidding participants. This implementation can help a purchaser to purchase a required product at a relatively low price, thereby saving the purchase cost.