AI Article Value Scoring Model for Content Quality
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Solution Overview
Problem
The existing methods for evaluating article value on information distribution platforms are ineffective, leading to a vicious cycle where high-quality articles are underrepresented, and low-quality articles dominate, threatening the quality of internet information resources.
Innovation Solution
A method and apparatus using artificial intelligence to evaluate article value by mining high-quality and low-quality articles as training data, training a value-scoring model, and performing feature extraction to determine the quality of a to-be-evaluated article based on extracted features and the model, with features including relevance, word count, and presence of images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If articles with less content are produced to attract user clicks, then user engagement increases, but article quality deteriorates
Solution Approach 1:
The system implements feedback by collecting user interaction data (clicks, reading time, likes, comments, shares) and using this feedback to train the AI model. The model continuously learns from user behavior patterns to better distinguish high-quality articles that genuinely engage users from low-quality clickbait, resolving the contradiction between engagement metrics and article quality
Solution Approach 2:
The patent replaces manual quality assessment mechanisms with an AI-based automated evaluation system. The neural network model automatically analyzes article features and user interaction patterns to assign quality scores, eliminating the need for manual review while maintaining or improving assessment accuracy in identifying high-value content
2Productivity
If more articles are recommended to users, then display opportunities increase, but information quality decreases
Solution Approach 1:
The system applies local quality by evaluating and recommending articles based on their individual quality scores generated by the AI model. Instead of uniform treatment of all articles, the system identifies and prioritizes high-quality articles within the recommendation stream, ensuring that increased display opportunities are allocated to content that meets quality standards
Solution Approach 2:
The patent changes the parameter of article evaluation from simple metrics like click-through rate to a comprehensive quality score that incorporates multiple factors including user engagement patterns, article content features, and reading behavior data. This parameter transformation enables the system to maintain information quality while increasing the volume of recommended articles
3Measurement precision
If manual annotation is used to identify high-quality articles, then training data accuracy improves, but processing time increases
Solution Approach 1:
The system performs preliminary action by pre-processing and annotating training data in advance using a combination of manual annotation for seed high-quality articles and automated techniques for expanding the training set. This preliminary preparation creates a robust foundation of labeled data that can be reused for model training, reducing the need for repeated manual annotation efforts
Solution Approach 2:
The patent employs copying by using manually annotated high-quality articles as templates to generate additional training data through duplication and variation. Once a set of accurately annotated articles is created, the system can replicate and adapt these examples to create larger training datasets, maintaining accuracy while reducing incremental annotation time
Data Source
AI summary
The present disclosure provides a method and apparatus for evaluating article value based on artificial intelligence, and a storage medium. The solution of present disclosure may be employed to pre-mine high-quality articles and low-quality articles as training data, and train according to the training data to obtain a value-scoring model. As such, value evaluation needs to be performed for the to-be-evaluated article, it is feasible to first perform feature extraction for the to-be-evaluated article, determine a score of the to-be-evaluated article based on the extracted features and the value-scoring model, and thereby implement effective evaluation of the article value.