Automatic Rating Optimization for Niche Content Visibility
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Solution Overview
Problem
Current program rating and recommendation systems fail to effectively guide viewers to rare or niche content, as they often lack ratings for such programs and provide recommendations that do not align with the unique preferences of niche viewers.
Innovation Solution
Automatic rating optimization system that receives ratings from multiple sources, adjusts weights based on the impact of these ratings on content popularity, and associates viewer preferences with rating sources to generate tailored and accurate ratings for niche content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If current program rating systems assign general ratings to programs, then typical viewer preferences are satisfied, but niche viewer preferences are not met and ratings cannot be trusted
Solution Approach 1:
The patent segments the viewer base into different preference profiles (niche viewers vs. typical viewers) and segments rating sources accordingly. By creating separate rating pathways for different viewer segments, the system provides tailored ratings that are both adaptable to specific preferences and reliable within each segment context
Solution Approach 2:
The system applies local quality by providing different rating qualities to different viewer segments. Niche viewers receive ratings optimized for their specific preferences while typical viewers receive general ratings, ensuring each group gets the appropriate level of customization and trustworthiness
2Quantity of substance
If ratings are provided by multiple rating sources, then more content can be rated, but it becomes difficult to determine which ratings to trust
Solution Approach 1:
The system implements feedback mechanisms where access events are counted and analyzed to determine how ratings from different sources affect program popularity. This feedback loop allows the system to continuously refine which ratings to trust and adjust weighting accordingly
Solution Approach 2:
The patent dynamically changes the weighting parameters of different rating sources based on observed effectiveness. Rating sources that produce higher engagement and positive outcomes receive increased weight, while less effective sources are downweighted, maintaining measurement precision across multiple sources
3Measurement precision
If access events are counted to determine rating effectiveness, then rating accuracy improves, but system complexity increases
Solution Approach 1:
The system performs self-service by automatically counting access events and using this data to adjust rating weights without requiring manual intervention. The automated feedback loop simplifies the overall complexity despite the sophisticated measurement capabilities
Data Source
AI summary
Automatic rating optimization is described. In an embodiment, ratings of a program can be received from one or more rating sources. Based on these ratings, a representation of a content selection mechanism can be sent to potential consumers of the content. Access events for the content can be counted over a duration of time so a determination can be made regarding how the ratings provided by each of the rating sources affect popularity of the content. A weight accorded to ratings received from each of the rating sources can be adjusted based on the determination. Profiles can be established for consumers and/or rating sources.