Web Feed Aggregator Reducing Content Redundancy
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Solution Overview
Problem
The increasing volume of web feeds makes it difficult for users to retrieve substantive and reliable updated information, as existing feed aggregators rely on manual tagging and filtering, which is time-consuming and ineffective for content that frequently changes, such as news websites.
Innovation Solution
An automated method and system for aggregating syndicated web content by retrieving, comparing, and storing updated content, using a similarity index to determine its similarity with stored content, and managing redundancy through thresholds, thereby eliminating redundant content and merging similar feeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual tagging and filtering are used to organize web feeds, then users can sort and filter articles into categories, but the process becomes time-consuming and ineffective for frequently changing content
Solution Approach 1:
The system performs automatic classification and filtering of web feed content using computational algorithms. The feed aggregator independently analyzes article content, computes similarity indices, and organizes feeds without requiring user intervention for tagging or categorization, thereby eliminating time-consuming manual pre-classification while maintaining effective content organization
Solution Approach 2:
The patent replaces manual mechanical tagging operations with automated computational processes. Instead of users manually assigning tags and categories, the system uses computer-based algorithms to compute similarity indices and automatically classify content, substituting human manual work with automated mechanical/computational systems
2Quantity of substance
If users subscribe to many web feeds to get comprehensive information, then information coverage increases, but the volume of articles becomes overwhelming and difficult to navigate
Solution Approach 1:
The system extracts and removes redundant content from the aggregated feeds by computing similarity indices between articles. Articles with high similarity scores are identified as duplicates or near-duplicates and are filtered out, leaving only unique and valuable content. This extraction of redundant information reduces the overall volume while preserving essential information
Solution Approach 2:
The patent implements a filtering mechanism that discards redundant articles based on similarity comparison. By comparing each new article against previously retrieved content and calculating similarity metrics, the system discards duplicate or highly similar articles, thereby reducing information overload while maintaining comprehensive coverage of unique content
3Loss of information
If all updated content from web feeds is stored to ensure completeness, then information completeness is maintained, but redundancy increases and storage efficiency decreases
Solution Approach 1:
The system implements a feedback mechanism where each newly retrieved article is compared against previously stored content using similarity index computation. This feedback loop allows the system to intelligently determine whether to store or discard new content based on its similarity to existing articles, thereby maintaining information completeness while eliminating redundancy through informed storage decisions
Data Source
AI summary
When aggregating syndicated Web content, updated content is retrieved from predetermined Web feeds. The updated content is compared with stored content previously retrieved. If the updated content is determined to be different from the stored content, the updated content is stored. If the updated content is determined to be identical to the stored content, the updated content is delete.