AI Domain Categorization via Webpage Feature Extraction
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Solution Overview
Problem
Manual categorization of webpages and domains is time-consuming, labor-intensive, and prone to errors, making it impractical to keep pace with the large number of new domains being created daily.
Innovation Solution
A classifier is trained using labeled training data to predict categories for webpages and domains, utilizing features like text and script analysis, allowing for automated and efficient categorization of new domains without human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human reviewers manually categorize each webpage, then categorization accuracy can be maintained, but the process becomes extremely time-consuming and labor-intensive
Solution Approach 1:
The system performs preliminary categorization using an AI classifier before human review. The classifier is pre-trained on labeled data to predict categories automatically, reducing the need for manual review of every webpage while maintaining accuracy through selective human verification of uncertain cases.
Solution Approach 2:
The system enables self-service categorization where the AI classifier independently categorizes webpages without human intervention. The classifier learns from training data and autonomously predicts categories for new domains, eliminating the need for continuous human review and significantly reducing time consumption.
2Measurement precision
If human reviewers categorize webpages, then categories can be accurately assigned, but the process is impractical given the huge number of new domains created daily
Solution Approach 1:
The system replaces the mechanical human review process with an AI-based classifier. The classifier uses machine learning algorithms to automatically analyze webpage features and assign categories, achieving both high accuracy and the throughput necessary to handle the vast number of new domains created daily.
Solution Approach 2:
The system changes the operational parameters from manual human review to automated AI classification. By training the classifier on diverse training data and adjusting classification thresholds, the system achieves parameter settings that simultaneously satisfy accuracy requirements and enable high-volume processing of new domains.
3Productivity
If automated classification is used, then processing speed increases, but accuracy may be reduced compared to human review
Solution Approach 1:
The system performs preliminary automated classification at high speed, then uses human review as a preliminary check for uncertain cases. This hybrid approach maintains the speed benefits of automation while ensuring accuracy through targeted human verification of borderline classifications.
Solution Approach 2:
The system implements feedback mechanisms where classification results are continuously evaluated and used to retrain and improve the classifier. Accuracy is enhanced through feedback loops that learn from both correct and incorrect classifications, allowing the system to maintain high speed while progressively improving precision.
Data Source
AI summary
In an embodiment, a set of labeled training data that includes indicators of webpages is received. Each indicated webpage is labeled with one or more categories that were determined for the webpage by a human reviewer. Features, such as text and scripts, are extracted from each indicated webpage, and are used along with the labels to train a classifier to predict one or more categories for a webpage based on the features of the webpage. The trained classifier may be used to associate one or more categories with each domain of a plurality of domains given the categories predicted for some or all of the webpages associated with the domain. A list of domains and associated categories may be used for a variety of purposes including search engine optimization and content filtering.