Content Discovery via Iterative Descriptor Weighting
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Solution Overview
Problem
Users face difficulty in discovering relevant content from large datasets due to the complexity of selecting appropriate descriptors, as existing systems do not effectively refine the descriptor set for efficient content discovery.
Innovation Solution
A content discovery application generates weights for descriptors based on frequency of selection and popularity, presenting users with a subset of relevant descriptors, which are then iteratively refined until a predefined condition is met, allowing for the recommendation of relevant content items and user interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If a large data set of descriptors is presented to users for content discovery, then the completeness of content coverage is improved, but the complexity of descriptor selection increases making it difficult for users to discover relevant content
Solution Approach 1:
The patent segments the large descriptor data set into smaller, manageable subsets based on frequency of selection and popularity metrics. Instead of presenting all descriptors at once, the system divides them into prioritized groups where the most relevant descriptors appear first, reducing the cognitive load on users while maintaining comprehensive content coverage.
Solution Approach 2:
The patent changes the parameter of descriptor presentation by sorting and filtering descriptors based on calculated weights derived from frequency of selection and popularity. This transforms the raw descriptor set into a refined sequence where high-value descriptors are prioritized, effectively reducing selection complexity without losing important content categories.
2Quantity of substance
If all descriptors are presented to users simultaneously, then the completeness of content categories is improved, but the time required for users to select relevant descriptors increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating weights for all descriptors based on historical frequency of selection and popularity data before presenting them to users. This pre-processing sorts descriptors in order of relevance, so users immediately see the most important options first, significantly reducing the time needed to make relevant selections while maintaining descriptor variety.
Solution Approach 2:
The patent replaces the mechanical approach of linearly scanning through all descriptors with an automated sorting mechanism that uses calculated weights. This substitution of manual browsing with algorithmic prioritization dramatically reduces selection time while preserving access to the complete descriptor variety through the weighted ranking system.
3Measurement precision
If the system refines descriptors based on user interactions, then the relevance of presented descriptors is improved, but the complexity of the content discovery system increases
Solution Approach 1:
The patent implements feedback mechanisms where user selections and interactions are continuously monitored and used to update the frequency of selection and popularity metrics. This feedback loop refines the descriptor weights over time, improving relevance by prioritizing descriptors that users actually choose, while the automated nature of the feedback processing keeps system complexity manageable.
Solution Approach 2:
The patent enables the system to self-refine descriptor relevance automatically using aggregated user interaction data without requiring manual curation. The system serves itself by continuously learning from user behavior patterns and automatically adjusting descriptor priorities, improving relevance while avoiding the complexity of manual descriptor management.
Data Source
AI summary
Disclosed are various embodiments for a content discovery application. Content items can be selected by first selecting a sequence of descriptors. The descriptors are selected from a subset of descriptors regenerated based on previously selected descriptors and other factors. The content item is selected from a pool of content items responsive to the sequence of descriptors. Users may explore data derived from sequences of descriptors and selected content items from other users to discover content relevant to their interests.