Geographic Relevance Component Using User Access Clustering
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Solution Overview
Problem
Current methods for determining the geographical relevance of web documents are labor-intensive, costly, or inaccurate, as they rely on human classification, automated parsing, or IP address location, which can fail to accurately identify documents relevant to specific geographic areas.
Innovation Solution
A method that determines the geographic relevance of web documents by analyzing the locations of multiple users accessing the documents, using cluster analysis on collected IP addresses to identify relevant geographic locations and adjust search results accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated parsers are used to analyze text for geographic terms, then the process becomes automated and faster, but accuracy deteriorates because geographic terms are often used for documents not relevant to specific geographic areas
Solution Approach 1:
The system uses observed user access patterns as feedback to refine and validate geographic relevance determinations. By monitoring which users actually access documents from which locations, the system continuously improves the accuracy of geographic associations beyond what automated parsing alone can achieve.
Solution Approach 2:
User access patterns serve as an intermediary between the automated parser and the final geographic relevance determination. Rather than relying solely on text analysis, the system uses actual user behavior data as a mediator to verify and refine geographic associations, resolving the accuracy problem.
2Extent of automation
If IP addresses of web servers are used to locate web documents, then the process becomes automated, but accuracy deteriorates because web documents may be hosted by servers remote from their geographic relevance
Solution Approach 1:
The system replaces server IP address feedback with user access pattern feedback. By monitoring which users access documents and their locations, the system obtains more accurate geographic relevance information that reflects where the document is actually relevant, rather than where it is hosted.
Solution Approach 2:
User access patterns act as an intermediary between the web document and its geographic location determination. This intermediary provides more accurate geographic information than server location, as users access documents from locations where the content is actually relevant to them.
3Measurement precision
If manual classification by humans is used to determine geographic relevance, then accuracy improves, but productivity deteriorates due to labor intensity and cost
Solution Approach 1:
The system uses user access patterns to automatically determine and validate geographic relevance without requiring manual classification. The collective behavior of users serves as self-generated data that automatically identifies geographic associations, eliminating the need for human reviewers while maintaining accuracy.
Solution Approach 2:
The system creates a feedback loop where user access patterns automatically inform and refine geographic relevance determinations. This automated feedback mechanism replaces manual classification, achieving both high accuracy and high productivity by using observed behavior rather than human judgment.
Data Source
AI summary
A geographic relevance component determines geographic relevance of web resources based on an analysis of data points that correspond to estimated physical locations of the IP addresses of a number of visitors to the web site. The geographic relevance component may additionally determine the probability that a particular user is within the geographical relevance corresponding to a web resource.