Tag Relevancy Rating System Using User Consensus
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Solution Overview
Problem
Current systems lack an effective method for providing accurate and user-driven relevancy ratings for tags associated with content, which affects search results and user grouping based on shared interests.
Innovation Solution
A system that utilizes user input to rate the relevancy of tags, storing these ratings in a data structure, and uses statistical models to determine a composite relevancy score, which is used to refine search results and group users based on shared content access and ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If user-driven tag relevancy ratings are implemented, then search result relevance and user grouping accuracy are improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments the tag relevancy rating process into distinct components: user rating input module, data structure for storing individual ratings, statistical model processing module, and result application module. This segmentation allows each component to handle specific tasks independently, managing complexity while maintaining measurement precision through specialized processing at each stage.
Solution Approach 2:
The patent introduces an intermediary statistical model that mediates between raw user ratings and the final search results or user grouping outcomes. This intermediary layer processes individual user-driven ratings through statistical computations to generate composite relevancy scores, thereby improving measurement precision while isolating the complexity of statistical processing from both data collection and result generation stages.
2Measurement precision
If statistical models process multiple user ratings to create consensus scores, then measurement precision of tag relevancy improves, but computational time and processing resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing individual user ratings in a structured data format as they are collected. This preliminary organization of rating data enables more efficient statistical processing later, reducing computational time when composite relevancy scores need to be generated while maintaining measurement precision through complete processing of all user inputs.
Solution Approach 2:
The patent applies parameter changes by transforming individual user rating parameters into composite relevancy score parameters through statistical models. This transformation process changes the state of the data from discrete user opinions to aggregated measurable metrics, improving measurement precision while the statistical nature of the transformation allows for efficient computation compared to alternative consensus methods.
3Loss of information
If user ratings are collected and stored for each tag, then information completeness about tag relevancy improves, but data storage requirements and system resource usage increase
Solution Approach 1:
The patent applies local quality by implementing a data structure that stores user ratings in an organized, localized manner specific to each tag-content pair. This structured local storage of rating information ensures completeness of tag relevancy data while optimizing storage efficiency through targeted data retention only where relevant, avoiding unnecessary data proliferation across the system.
Solution Approach 2:
The system creates a composite data structure that combines multiple individual user ratings into a unified representation of tag relevancy. This composite approach preserves the completeness of information from all user inputs while consolidating the data into an efficient storage format that reduces overall data volume compared to storing separate unprocessed rating records for each user-tag interaction.
Data Source
AI summary
A content item may be associated with metadata comprising one or more tags. A user may indicate a relevance rating associated with a tag. The relevance rating may indicate whether the user feels the tag is relevant to a particular content item. Using a plurality of user-provided relevance ratings, a tag relevance model may be established. A tag relevance model may comprise a weighted or un-weighted average and/or median relevance rating of the tag and/or a consistency of the relevance rating. The tag relevance model may be used to order or otherwise inform search results. Tag ratings may also be used to aggregate users into groups comprising users having a similar point of view relative to one or more tag ratings. In addition, users may be grouped according to content access and/or tags rated regardless of the relevance rating applied.