AI Low-Quality Article Recognition via User Feedback Features
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Solution Overview
Problem
Manual checking of articles in news-recommending systems for low-quality content is time-consuming and inefficient, leading to low recognition efficiency and high manpower costs.
Innovation Solution
A method and apparatus using artificial intelligence to recognize low-quality articles by obtaining user feedback behavior features and utilizing a predetermined recognition model, which includes training with user feedback data to classify articles as low-quality or not, employing multiple classifier models and adjusting parameters for improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual checking is used to identify low-quality articles, then recognition accuracy can be maintained through human judgment, but recognition efficiency becomes extremely low and time consumption increases significantly
Solution Approach 1:
The patent replaces the manual mechanical checking process with an AI-based automated recognition system. The system uses a recognition model that processes article features (text content, user feedback behavior, engagement metrics) to automatically classify articles as low-quality or not, eliminating the need for human manual review while maintaining recognition accuracy through trained machine learning algorithms
Solution Approach 2:
The system enables self-service by allowing the AI model to autonomously perform the entire recognition process without human intervention. The model automatically learns from training data, processes new articles independently, and makes classification decisions on its own, replacing the manual service of human editors or moderators
2Reliability
If all articles are manually checked to prevent low-quality content, then content quality can be maintained, but manpower costs and time resources increase significantly
Solution Approach 1:
The patent substitutes manual human checking with an automated AI-based recognition system that processes articles at scale. The system uses feature extraction (text analysis, user feedback patterns, engagement metrics) and classification algorithms to maintain content quality without requiring extensive human time investment, thereby reducing time loss while preserving reliability
Solution Approach 2:
The AI recognition system operates continuously without interruption, processing articles as they are published without requiring scheduled human review cycles. This continuous automated operation maintains consistent content quality standards while eliminating the time loss associated with manual checking cycles
3Measurement precision
If manual checking is performed for low-quality article recognition, then recognition accuracy can be maintained through human expertise, but the process becomes arduous and efficiency becomes very low
Solution Approach 1:
The patent replaces the arduous manual operation with an automated AI system that handles the entire recognition process. The system automatically extracts features from articles, analyzes user feedback behavior patterns, and applies classification models to identify low-quality content, eliminating the physical and mental effort required for manual checking while maintaining recognition accuracy through sophisticated algorithms
Data Source
AI summary
A method and apparatus for recognizing a low-quality article based on artificial intelligence, a device and a medium. The method comprises: obtaining a user feedback behavior feature of a to-be-recognized article in a news-recommending system; according to the user feedback behavior feature of the to-be-recognized article and a predetermined low-quality article recognition model, recognizing whether the to-be-recognized article is a low-quality article. Automatically recognizing whether the to-be-recognized article is a low-quality article according to the user feedback behavior feature of the to-be-recognized article and the predetermined low-quality article recognition model, thereby overcoming the technical problem about consumption of time and effects and low recognition efficiency in manually checking whether the to-be-recognized article is a low-quality article in the prior art, not only substantially saving the time spent in recognizing whether the to-be-recognized article is the low-quality article, saving manpower consumed in recognition, improving the recognition efficiency of the low-quality article.