Topic Page Moderation via Confidence Levels
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Solution Overview
Problem
Bucket testing for web page optimization is inefficient for topic pages with insufficient user interactions, making it difficult to determine if changes improve or diminish the page's quality, leading to tedious and time-consuming processes.
Innovation Solution
A moderation algorithm evaluates topic page modifications based on confidence levels, implementing changes that enhance the page's quality and discarding those that do not, allowing for user customization without relying on large user interaction datasets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If bucket testing is used to determine topic page optimization, then user interaction data can validate page changes, but it requires large samples of user data which makes it impractical for infrequently accessed pages
Solution Approach 1:
The patent introduces a moderation algorithm as an intermediary between user interactions and topic page modifications. This algorithm uses confidence levels (derived from multiple factors including user interactions, content quality metrics, and editorial review) to determine whether modifications should be implemented, eliminating the need to wait for large buckets of user data to accumulate before making optimization decisions
Solution Approach 2:
The system performs preliminary evaluation of topic pages and modifications through the moderation algorithm before actual user testing. By pre-assessing confidence levels and potential impact, the system can proactively implement or reject modifications without waiting for extensive user interaction data to accumulate, thus resolving the time delay inherent in traditional bucket testing
2Adaptability or versatility
If user customizations are allowed to improve topic page relevance, then user engagement increases, but quality control becomes difficult without large datasets to evaluate changes
Solution Approach 1:
The moderation algorithm implements a feedback mechanism that continuously monitors and evaluates user customizations against multiple criteria including confidence levels, content quality metrics, and performance data. This feedback loop ensures that only quality improvements are retained while maintaining customization flexibility, as the system learns from each modification's impact on topic page performance
Solution Approach 2:
The system dynamically adjusts confidence levels and moderation thresholds based on accumulated data and performance metrics. As more data becomes available, the system can become more or less restrictive in its quality control, allowing adaptable quality assurance that responds to changing conditions rather than relying on fixed, rigid rules
Data Source
AI summary
Methods for guiding user moderation at a topic page by confidence levels includes presenting a topic page in response to a query. The topic page includes a plurality of modules with content that match the query. The topic page is associated with a confidence level and with one or more page attributes that define the characteristics of the topic page and the modules included therein. One or more modifications to the topic page are received as part of customization of the topic page. The modifications include a plurality of page attributes that define the modification and a plurality of user attributes of a user performing the modification. The modifications are evaluated based on the page attributes and user attributes including confidence levels associated with the topic page and the user. The modifications are implemented based on the evaluation. The implemented modifications enhance the quality and confidence level of the topic page.