Self-Learning Platform for Page Content Accuracy
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Solution Overview
Problem
Conventional approaches for maintaining the accuracy of content in computerized networking systems, such as social networks, are inadequate as the number of content items increases exponentially, leading to inaccuracies in fields like phone numbers, hours of operation, and physical addresses, which are not effectively addressed by existing methods.
Innovation Solution
An active self-learning platform evaluates candidate values for fields in pages using machine learning models, incorporating user endorsements and data pipeline consensus, to determine and automatically update the most accurate values, ensuring accuracy and completeness of information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional approaches are used to maintain content accuracy, then the system can operate with existing methods, but accuracy deteriorates as the number of content items increases exponentially
Solution Approach 1:
The system employs automated machine learning models to self-evaluate and self-correct content accuracy without requiring manual verification for each item. The machine learning model automatically processes large volumes of content, evaluates candidate values for fields like phone numbers, hours of operation, and physical addresses, and updates accuracy information autonomously as content volume increases
Solution Approach 2:
The system incorporates feedback mechanisms where user endorsements and data pipeline consensus are used to continuously improve the machine learning model's accuracy assessments. The model learns from user interactions and adjusts its evaluations, creating a feedback loop that maintains high accuracy despite exponential growth in content items
2Measurement precision
If manual verification of content accuracy is performed, then accuracy can be maintained for individual items, but the system becomes inefficient and cannot scale with content volume
Solution Approach 1:
The patent replaces manual mechanical verification processes with automated machine learning models that can process and evaluate content accuracy at scale. The machine learning model substitutes human manual checking, enabling precise accuracy measurement across millions of content items while maintaining high system efficiency and productivity
3Productivity
If the system automatically updates content fields, then productivity increases, but the complexity of the evaluation and update mechanism increases
Solution Approach 1:
The system segments the content evaluation process into distinct components: candidate value generation, machine learning model evaluation, user endorsement verification, and automatic update mechanisms. This segmentation allows each component to be optimized independently, managing overall system complexity while maintaining high productivity in content field updates
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine a set of candidate values for a field in a page. The set of candidate values can be evaluated for accuracy based at least in part on a machine learning model, wherein the machine learning model outputs a respective score for each candidate value that measures an accuracy of the candidate value for the field in the page. A best scoring candidate value can be determined from the set of candidate values. The field in the page can be associated with the best scoring candidate value.