Content Discovery System Using Community Rating and User Profiling
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Solution Overview
Problem
Users face difficulties in finding relevant content online due to lack of effective categorization, quality assessment, and discovery mechanisms, making it hard to identify expert authors, updated information, and shared interests.
Innovation Solution
A computer system that allows users to categorize and rate content using keywords and geographies, with community and expert ratings, and employs a suggested reading engine to recommend content based on user profiles and behavior, while providing compensation to authors based on quality and popularity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users publish content freely on the internet, then content availability and diversity increase, but content discoverability and quality assessment deteriorate
Solution Approach 1:
The patent segments content into categorized collections with hierarchical structures (e.g., topics, subtopics, tags). Content is divided into manageable units that can be independently organized and searched, making the vast amount of internet content discoverable through structured navigation rather than overwhelming users with raw volume.
Solution Approach 2:
The patent introduces an intermediary rating system that mediates between content creators and consumers. Expert raters and community members act as intermediaries who assess and rate content quality, providing a trusted layer that helps users navigate and evaluate content without directly examining every piece themselves.
2Ease of operation
If content is categorized using fixed taxonomies, then content organization improves, but adaptability to new topics and user needs deteriorates
Solution Approach 1:
The patent implements a dynamic categorization system where taxonomies can evolve over time. New topics, tags, and categories can be added based on emerging content trends and user feedback. The system adapts its structure dynamically rather than being locked into static classifications, allowing it to grow with the content ecosystem.
Solution Approach 2:
The patent creates a universal tagging system that works across diverse content types and topics. Tags serve multiple functions: they organize content, enable searching, track trends, and facilitate recommendation. This multi-functional approach allows the same categorization mechanism to handle both established and emerging topics effectively.
3Measurement precision
If community rating systems are implemented, then content quality assessment improves, but system complexity and moderation burden increase
Solution Approach 1:
The patent implements a tiered rating system where not all content requires full community review. Expert raters provide initial assessments for high-stakes or controversial content, while routine content receives streamlined evaluation. This partial action approach maintains quality assessment without requiring exhaustive review of every piece of content, reducing overall system complexity.
4Productivity
If author compensation is based on content popularity, then author motivation improves, but content quality diversity deteriorates
Solution Approach 1:
The patent implements local quality control by introducing expert raters who specialize in different content domains. Each expert evaluates content within their area of expertise, ensuring that quality standards are maintained locally within each topic area rather than applying a single uniform metric across all content. This preserves diversity while maintaining overall quality.
Data Source
AI summary
A computer system for publishing content includes a categorization member, a rating module, a user profiling assembly and a suggested-reading engine. The categorization member enables an author-user and reader-users of an authored work to categorize content of the authored work using a fixed taxonomy, keywords, tags and/or keyword combinations. The rating module enables reader-users to rate authored works and makes a determination of quality of each authored work in the system. Rating includes reader-user response activity such as number of times the reader recommends, forwards or otherwise effectively promotes the authored work. The user profiling assembly measures words and word combinations used (written or read) by a user and generates therefrom a profile of the user. The suggested-reading engine is responsive to the rating module and provides a ranked list of suggested reading (authored works) for a user. The suggested reading is based on the user's ratings of authored works, respective relevance of authored works to the user and editorial promotion of certain articles/authored works.