Compliance Model Training for Landing Page Violation Classification
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Solution Overview
Problem
Existing systems face challenges in identifying and restricting online content that violates distribution guidelines, as determining violating advertisements is subjective and requires frequent analysis of changing landing pages, making it difficult to maintain accuracy and compliance.
Innovation Solution
A method involving training compliance models using manual classifications and feature values of content items, with iterative updates based on manual classifications of suspicious items, to classify content items as violating, suspicious, or complying, ensuring accuracy measures meet thresholds, and preventing distribution of violating content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual classification methods are used to identify violating advertisements, then classification accuracy can be maintained, but the system complexity and time consumption increase significantly
Solution Approach 1:
The system segments the classification task into multiple stages: an automated compliance model performs initial classification of content items, and only items classified as suspicious are forwarded to manual classification by raters. This segmentation reduces the overall system complexity by automating the majority of classifications while maintaining accuracy through selective human review.
Solution Approach 2:
The compliance model acts as an intermediary between the content items and the manual classification process. It pre-processes and filters content items, identifying suspicious ones that require manual review. This intermediary layer reduces the burden on manual classifiers and maintains system accuracy by ensuring only uncertain cases reach human reviewers.
2Measurement precision
If manual classification is used for all content items, then classification accuracy is maintained, but the productivity and processing speed decrease
Solution Approach 1:
The classification workflow is segmented into automated and manual phases. The compliance model rapidly processes all content items automatically, and only suspicious items are segmented out for manual classification. This approach maintains high processing speed for the majority of items while preserving accuracy through targeted manual review.
Solution Approach 2:
Instead of applying manual classification to all content items (excessive action), the system applies manual classification only to suspicious items identified by the compliance model (partial action). This partial manual intervention maintains classification accuracy where needed while preserving overall processing speed.
3Measurement precision
If the compliance model is updated frequently with new training data, then classification accuracy improves, but the loss of time and computational resources increase
Solution Approach 1:
The compliance model is updated periodically using newly classified content items as training data. Rather than continuous updates, the system accumulates training data over time and performs periodic model retraining, which maintains classification accuracy while reducing the time and computational overhead associated with frequent updates.
Solution Approach 2:
The system performs preliminary classification using the existing compliance model before accumulating sufficient data for the next model update. This preliminary action allows the system to continue operating with current model capabilities while preparing training data in the background, minimizing disruption and time loss.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for analyzing content item compliance with specified guidelines. In one aspect, a method includes receiving training data that specify manual classifications of content items and feature values for each of the content items, where each manual classification specifies whether the content item is a violating content item. Using the training data, a compliance model is trained to classify an unclassified content item as a violating content item based on the feature values of the unclassified content item. A determination is made that the compliance model has an accuracy measure that meets a threshold accuracy measure. In response to determining that the accuracy measure for the compliance model meets the accuracy threshold, unclassified content items are classified using the feature values for the unclassified content items, and data specifying the classifications are provided.