Article Relevance Scoring via Visitor Metrics
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Solution Overview
Problem
Current web analytics tools do not provide publishers with insights on how to frame articles to fit the interests of specific audiences, despite offering reports on article popularity, leaving authors to sift through numerous irrelevant articles to find relevant content.
Innovation Solution
A system that analyzes online articles to generate article scores based on relevance to user-selected topics and visitor metrics, ranking articles and identifying important terms for presentation to users, allowing for the identification of relevant and popular content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If web analytics tools provide reports on article popularity, then publishers can identify popular articles, but the tools fail to provide insights on how to frame articles to fit specific audience interests
Solution Approach 1:
The system extracts specific useful information (important terms and framing insights) from the large body of article data, separating the essential knowledge needed for article creation from the overwhelming volume of raw article content that authors would otherwise need to review manually
Solution Approach 2:
The system acts as an intermediary between the large corpus of published articles and the author, processing and transforming raw article data into actionable insights about audience interests and effective article framing, thereby bridging the gap between data and decision-making
2Measurement precision
If authors review numerous articles to find relevant content, then they can identify relevant topics, but the process requires significant time and effort
Solution Approach 1:
The system performs preliminary analysis of articles to pre-identify important terms and relevance patterns before authors need this information, so when authors query for relevant content, the analysis is already complete and ready for immediate use
Solution Approach 2:
The system creates simplified representations (copies) of article relevance and importance in the form of extracted terms and metrics, allowing authors to access the essential information without examining the full original articles
Data Source
AI summary
Systems and methods provide for analyzing a group of online articles to identify relevant and popular online articles given a selection of topic(s) and/or term(s). An article score is generated for each online article based on the selected topic(s) and/or term(s) as a function of the relevance of the topic(s) and/or term(s) to the online article and visitor metrics for the online articles. The online articles are ranked based on the article scores, and an indication of the ranked online articles is provided for presentation to the user. In further embodiments, important terms are identified for a selection of topic(s) and/or term(s) based on the most relevant and popular online articles for the selected topics/terms.