Regression Model System for Product Description Quality Assessment
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Solution Overview
Problem
Existing methods for assessing the quality of product description text in e-commerce are hindered by confounding factors like product price and brand, subjectivity in manual evaluation, and inability to adapt to changing aesthetic and fashion trends over time.
Innovation Solution
A system that uses regression models to evaluate the quality of product description text by training models on text content and confounding features, calculating residual losses to isolate the importance of text content, and employing continuous learning to update models based on changing data and prediction errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If confounding features (brand, price, shop reputation) are included in the assessment model, then the model can capture overall product performance factors, but the text content quality assessment becomes inaccurate due to confounding effects
Solution Approach 1:
The assessment model is segmented into two distinct components: a first regression model that evaluates overall product performance using all features (including confounding features), and a second regression model that evaluates only confounding features. This segmentation allows the system to isolate and remove the confounding effects from the final text quality assessment, thereby improving measurement precision while accounting for relevant product information.
Solution Approach 2:
The system extracts and removes the confounding effects (brand, price, shop reputation) from the assessment by calculating the difference between the first model's predictions (which include confounding features) and the second model's predictions (which include only confounding features). This extraction process isolates the pure text content quality metric, eliminating the harmful confounding effects while preserving the informative aspects of product performance.
2Measurement precision
If manual evaluation methods are used to assess text quality, then subjective human judgment can be applied, but the assessment process is time-consuming and inconsistent
Solution Approach 1:
The system replaces the mechanical manual evaluation process with an automated regression-based assessment model. The first regression model and second regression model automatically evaluate text quality by processing product information through mathematical functions, eliminating the need for time-consuming manual review while providing consistent and objective assessments across all product descriptions.
Solution Approach 2:
The assessment system performs self-service evaluation by automatically processing product information through the regression models without requiring external human intervention. The models independently calculate the text quality metric by comparing the first model's predictions with the second model's predictions, enabling rapid and consistent assessment of large volumes of product descriptions.
3Adaptability or versatility
If static assessment models are used, then the model structure remains simple, but the system cannot adapt to changing aesthetic and fashion trends over time
Solution Approach 1:
The assessment system transitions from a static model to a dynamic model that can adapt to changing trends. The first regression model and second regression model are continuously updated with new product information and feedback data, allowing the system to capture evolving aesthetic preferences and fashion trends. This dynamic updating mechanism ensures the assessment remains relevant and accurate over time while maintaining a relatively simple regression-based structure.
Solution Approach 2:
The system incorporates feedback mechanisms where the assessment results and new product data are used to retrain and update the regression models. By continuously feeding new information back into the first model and second model, the system adapts to changing aesthetic and fashion trends, improving its adaptability while using the feedback loop to manage complexity efficiently.
Data Source
AI summary
A system for assessing text content of a product. The system includes a computing device having a processor and a storage device storing computer executable code. The computer executable code, when executed at the processor, is configured to: provide text contents and confounding features of products; train a first regression model using the text content and the confounding features of the products; train the second regression model using the confounding features; operate the first regression model using the text contents and the confounding features to obtain a total loss; operate the second regression model using the confounding features of to obtain a partial loss; subtract the total loss from the partial loss to obtain a residual loss; use the residual loss to evaluate models and parameters for the regression models; and use the first regression model to obtain log odds of the words indicating importance of the words.