Machine Learning Models for Automated Advertisement Qualitative Rating Prediction
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Solution Overview
Problem
Conventional methods for evaluating advertisement effectiveness on social networking systems require significant time and resources, as they rely on human reviewers to provide qualitative ratings, which are inefficient and not scalable.
Innovation Solution
The use of machine learning models, specifically neural networks and regression models, to analyze visual content of advertisements and automatically determine qualitative ratings such as noticeability, focal point, emotional reward, and call-to-action, reducing the need for extensive human review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reviewers are used to provide qualitative ratings for advertisements, then measurement precision is improved, but productivity deteriorates and loss of time increases
Solution Approach 1:
The patent replaces the mechanical human review process with an automated machine learning system that uses computer vision to analyze advertisement images and predict qualitative ratings. The system processes images through neural networks to generate predictions for multiple rating criteria simultaneously, eliminating the need for human reviewers while maintaining scalable evaluation capabilities.
2Measurement precision
If human reviewers are used to provide qualitative ratings for advertisements, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system substitutes human time investment with automated computational processing. Multiple rating predictions are generated simultaneously through parallel processing of advertisement images through trained machine learning models, reducing evaluation time from hours or days to seconds while maintaining consistent quality standards.
3Productivity
If machine learning models are used to predict qualitative ratings, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex rating evaluation task into multiple specialized machine learning models, each trained to predict specific qualitative rating criteria (e.g., noticeability, emotional reward, call-to-action). This segmentation allows each model to focus on specific aspects of advertisement quality, making the overall system more manageable and interpretable while maintaining high productivity.
4Productivity
If machine learning models are used to predict qualitative ratings, then productivity is improved, but reliability may deteriorate
Solution Approach 1:
The patent combines multiple machine learning model predictions into a comprehensive qualitative rating assessment. By integrating predictions from several specialized models that evaluate different aspects of advertisement quality, the system achieves more reliable and balanced ratings that compensate for individual model limitations while maintaining high productivity.
Data Source
AI summary
Systems, methods, and non-transitory computer readable media can determine a representation of an advertisement based on a first machine learning model. The representation can be provided to a second machine learning model. One or more qualitative ratings associated with the advertisement can be determined based on the second machine learning model.