Automated Sitemap Maintenance Through Quality-Based Page Classification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Websites face challenges in managing sitemaps due to the presence of low-quality webpages that hinder effective indexing by search engines, leading to reduced visibility and user navigation.
Innovation Solution
A system and method that tracks interaction data, determines content and link equity scores, and classifies webpages to identify and remove low-quality pages from the sitemap, using algorithms like logistic regression to predict ranking probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all webpages are included in the sitemap, then comprehensive indexing coverage is achieved, but low-quality webpages reduce overall indexing effectiveness and visibility
Solution Approach 1:
The patent extracts and removes low-quality webpages from the sitemap based on quality assessments using machine learning models. The system identifies pages with poor content quality, low engagement metrics, or insufficient authority and excludes them from sitemap submission to search engines, thereby improving the overall quality and effectiveness of indexed pages.
Solution Approach 2:
The patent applies different quality thresholds and assessment criteria to different types of webpages within the website. Instead of treating all pages uniformly, the system evaluates each page individually based on its specific characteristics, content type, and intended purpose, allowing high-quality pages to be prioritized while low-quality pages are removed.
2Loss of information
If search engines index all webpages, then complete website coverage is achieved, but low-quality pages dilute the visibility of high-quality pages
Solution Approach 1:
The patent extracts low-quality webpages from the indexable set by removing them from the sitemap. This extraction process ensures that search engines focus their crawling and indexing resources on high-quality pages, preventing low-quality pages from diluting the visibility and ranking potential of superior content.
Solution Approach 2:
The patent performs preliminary quality assessment and filtering of webpages before sitemap generation and submission to search engines. By pre-evaluating pages using machine learning models that assess content quality, engagement metrics, and authority signals, the system prepares an optimized sitemap that maximizes the chances of high-quality pages being indexed and ranked favorably.
3Measurement precision
If manual quality assessment of webpages is performed, then accurate identification of low-quality pages is achieved, but the process becomes time-consuming and resource-intensive
Solution Approach 1:
The patent replaces manual quality assessment with automated machine learning models that evaluate webpage quality based on multiple factors including content analysis, engagement metrics, and authority signals. These models process and assess thousands of pages simultaneously and continuously, providing accurate quality measurements without the time and resource constraints of manual review.
Solution Approach 2:
The patent implements a self-updating machine learning system that continuously learns from new data and improves its quality assessment accuracy over time. The models automatically adapt to changing quality standards and user behavior patterns, maintaining high measurement precision without requiring ongoing manual calibration or intervention.
Data Source
AI summary
A system comprising one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform operations: classifying one or more webpages of a website into one or more classifications using interaction data, a content score, and a link equity score, each for the one or more webpages and removing, based on the one or more classifications, the one or more webpages from the website and from a sitemap of the website. Other embodiments are disclosed herein.