Audience Extraction System Using ML Effect Parameter Estimation
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Solution Overview
Problem
Current methods for extracting audience population packages for network media information are inefficient, labor-intensive, and lack precision due to subjective label classification and excessive manual involvement, resulting in low click-through and conversion rates.
Innovation Solution
A method and system that collect historical distribution effect data, acquire and improve attribute data, construct and train an effect parameter estimation model, and automatically select audiences with high estimated effect parameter values to form targeted audience population packages, reducing manual labor and enhancing precision.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual label classification and extraction methods are used for audience population packages, then the process allows human judgment and flexibility, but the extraction efficiency is low and labor costs are high
Solution Approach 1:
The patent replaces manual mechanical classification operations with an automated machine learning system. The ML model automatically processes audience data, performs label classification, and extracts population packages without human intervention, thereby substituting the mechanical manual system with an automated computational system that maintains flexibility through algorithmic decision-making.
Solution Approach 2:
The system enables self-service automation where the ML model independently performs data processing, classification, and population package extraction without requiring manual operations. The automated system serves itself by automatically training on historical data, making predictions, and generating results, eliminating the need for continuous human labor while maintaining operational flexibility.
2Reliability
If manual involvement is excessive in the extraction process, then the process allows for careful review and adjustment, but the labor cost and time consumption increase significantly
Solution Approach 1:
The patent implements preliminary action by pre-training the machine learning model on historical distribution effect data before actual population package extraction. The model is prepared in advance with learned patterns and relationships, enabling it to perform rapid, reliable extractions without requiring manual review during the actual extraction process, thus reducing time loss while maintaining reliability through pre-validated algorithms.
Solution Approach 2:
The system incorporates feedback mechanisms where the ML model continuously learns from historical distribution effects and adjusts its classification criteria accordingly. This feedback loop ensures reliability by automatically refining the extraction process based on past performance data, eliminating the need for manual review while maintaining or improving accuracy over time.
3Adaptability or versatility
If subjective label classification is used, then the process allows for human expertise and judgment, but the precision and objectivity of audience segmentation decrease
Solution Approach 1:
The patent transforms subjective human judgment into objective measurable parameters by using the machine learning model to process and classify audience data based on learned patterns from historical effects. The model converts qualitative human expertise into quantitative parameters and algorithms, maintaining adaptability through flexible model configuration while achieving precise, objective segmentation through computational processing.
4Device complexity
If traditional distribution methods are used, then the system is simple to implement, but the click-through rate and conversion rate remain low
Solution Approach 1:
The patent applies universality by creating a multi-functional machine learning system that performs multiple tasks: data processing, audience segmentation, population package extraction, and effect prediction. This universal system replaces multiple separate traditional processes with a single integrated solution that maintains ease of implementation through unified architecture while significantly improving distribution effectiveness through intelligent algorithms.
Data Source
AI summary
A method and a system for processing network media information are provided. The method includes: collecting historical distribution effect data of network media information; performing attribute improvement processing on the historical distribution effect data by using population attribute data and network media information management data to obtain characteristic attribute data; constructing an effect parameter estimation model corresponding to each attribute of the characteristic attribute data, and training the effect parameter estimation model; estimating an effect parameter value of each audience for target network media information according to the effect parameter estimation model; and selecting an audience whose estimated effect parameter value is greater than or equal to a specified threshold value to form an audience population package to be extracted. The present invention can improve efficiency of extraction, reduce labor cost, and improve precision of distribution of network media information in an audience population package.