ML Classifier for Blog Page Identification
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Solution Overview
Problem
Search engines face difficulties in identifying blog pages due to the vast number and diverse nature of blogs on the internet, making it challenging to accurately classify and categorize them.
Innovation Solution
A machine learning classifier is trained using features such as hosting domain, non-HTML markup words, outgoing link targets, URL strings, and the presence of ATOM or RSS feeds to classify web pages as blogs or non-blogs, with the ability to refine initial classifications using additional features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional search engine methods are used to identify blog pages, then the search process is simple and fast, but the accuracy of blog page identification is low
Solution Approach 1:
The patent segments the blog identification task into multiple feature categories (hosting domain, non-HTML markup, outgoing links, URL strings, ATOM/RSS feeds) and processes each category separately through a machine learning classifier. This segmentation allows the system to analyze complex blog characteristics systematically while maintaining manageable computational complexity.
Solution Approach 2:
The patent performs preliminary classification of web pages using a trained machine learning model before final search results are generated. By pre-identifying blog pages through multiple feature analysis and refining initial classifications, the system prepares data in advance, improving identification accuracy without significantly impacting search speed.
2Measurement precision
If multiple features are used to classify web pages as blogs, then the classification accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent implements a two-stage classification process where some features are used for initial classification and additional features are used to refine the classification. This partial action approach allows the system to achieve high accuracy by selectively applying computational resources - using all features when needed but not always requiring the full computational burden for every page.
Solution Approach 2:
The machine learning classifier dynamically adjusts its analysis based on the input features from different categories. By changing parameters such as which features to analyze and the depth of analysis required, the system can balance between processing speed and classification accuracy, adapting to different web page types and characteristics.
Data Source
AI summary
A machine learning classifier is used to determine whether a web page belongs to a blog, based on a number of characteristics of web pages (e.g., presence of words such as “permalink”, or being hosted on a known blogging site). The classifier may be initially trained using human-judged examples. After classifying web pages as being blog pages, the blog pages may be further identified or categorized as top level blogs based on their URLs, for example.