A method for identifying defective products in e-commerce based on transfer learning

Abstract

The present invention provides a method for identifying defective products in e-commerce based on transfer learning, comprising the following steps: S30 Encoding text information, encoding the text information into vector form to obtain encoded data, and using words in the text information in the current paragraph The co-occurrence in , to obtain a distributed representation; S40 feature extraction, input the encoded data into the algorithm module, and generate domain-independent features through the migration learning algorithm based on confrontational domain adaptation; and S50 defective product identification, domain-independent The feature is used as input, through a multi-channel convolutional neural network, and then connected to multiple layers of fully connected layers to identify defective products. A method for identifying defective products in e-commerce based on transfer learning of the present invention aims at the difference between product data distributions on different e-commerce platforms, uses the transfer learning method to greatly reduce the cost of identifying defective products on different e-commerce platforms, and improves The accuracy of defective product identification is improved.

Description

technical field [0001] The invention relates to the technical field of e-commerce data analysis, in particular to a method for identifying defective e-commerce products based on transfer learning. Background technique [0002] With the development of the Internet and the advent of the era of artificial intelligence, information exchange has become increasingly frequent, resulting in a huge increase in the total amount of information. In the context of the widespread application of e-commerce, the massive data of the major e-commerce platforms behind it will have inestimable mining value. Although e-commerce is developing rapidly, there are still many problems. One of the important issues is the quality of e-commerce products. [0003] With the emergence of various e-commerce platforms, how to realize defective product identification on different e-commerce platforms is a challenge. Different e-commerce platforms have differences in product descriptions and buyer reviews. ...

AI Technical Summary

Problems solved by technology

Different e-commerce platforms have differences in product descriptions and buyer reviews. The defective product identification method adopted based on the data of a specific platform such as Taobao can be directly used on another platform such as JD.com to identify defective products. degree of reduction

However, the cost of re-customizing a specific defective product identification method based on Jingdong’s product data is too high. How to use Taobao’s defective product identification method to identify Jingdong’s products has become a technical problem.

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