Automated E-commerce Copy Generation Using Quality Screening
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Solution Overview
Problem
Existing methods for generating high-quality product descriptions in e-commerce are inefficient and costly, requiring manual effort and lacking accurate quality measurement methods.
Innovation Solution
A method and device for automatically generating product descriptions using a trained copy generation model, which acquires attribute data, determines key attributes, generates candidate copies, and screens them based on quality determination rules to produce high-quality, fitting product descriptions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual copy writing is used, then copy quality can be ensured, but the cost is high and efficiency is low
Solution Approach 1:
The system performs preliminary actions by pre-training a copy generation model with extensive product attribute data and quality determination rules before actual copy generation. This allows the model to automatically generate high-quality copies without manual intervention during the actual product description process, resolving the contradiction between quality and efficiency
Solution Approach 2:
The invention uses a trained copy generation model that learns from existing high-quality product descriptions and attributes. The model copies and adapts proven writing patterns and structures to generate new product copies automatically, maintaining quality standards while eliminating manual writing requirements
2Reliability
If manual copy writing is used, then copy quality can be ensured, but it cannot quickly cover a large number of commodities
Solution Approach 1:
The system uses a trained copy generation model that automatically generates product descriptions by copying and adapting from learned patterns in training data. This allows simultaneous generation of copies for large numbers of commodities without manual intervention, eliminating the time loss while maintaining quality through the model's learned standards
Solution Approach 2:
The system changes the parameter of copy generation from manual text input to automated model inference. By adjusting the input attributes and using the trained model, copies are generated rapidly for multiple commodities while quality is maintained through consistent application of learned writing standards and quality determination rules
3Productivity
If automated copy generation is used, then efficiency is improved, but quality measurement is inaccurate
Solution Approach 1:
The system implements feedback mechanisms by using quality determination rules to evaluate generated copies and select the best ones. The model receives feedback from the quality assessment process, allowing it to improve future generations while maintaining high efficiency and accurate quality measurement through automated evaluation
Solution Approach 2:
The system performs preliminary action by pre-establishing comprehensive quality determination rules and training the model with quality-labeled data before deployment. This preliminary quality framework enables accurate measurement of generated copies without sacrificing generation efficiency, as the evaluation is automated and integrated into the generation process
Data Source
AI summary
A copy generation method and apparatus, an electronic device, a computer storage medium and a computer program product. The method comprises: acquiring first attribute data of a commodity (100); determining first key attribute data of the commodity on the basis of a pre-trained first copy generation model, wherein the first key attribute data represents part of the first attribute data (101); obtaining a first candidate copy set for the commodity according to the first key attribute data, wherein the first candidate copy set represents a set of at least one piece of commodity copy (102); and screening candidate copy data according to a quality determination rule, and determining a target commodity copy, wherein the candidate copy data comprises the commodity copy in the first candidate copy set (103).