Developing Data-Conscious Deep Learning Models for Product Classification
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Published:2021-06-19
Issue:12
Volume:11
Page:5694
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ISSN:2076-3417
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Container-title:Applied Sciences
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language:en
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Short-container-title:Applied Sciences
Author:
Kim Yijin,
Lee Hong Joo,
Shim JunhoORCID
Abstract
In online commerce systems that trade in many products, it is important to classify the products accurately according to the product description. As may be expected, the recent advances in deep learning technologies have been applied to automatic product classification. The efficiency of a deep learning model depends on the training data and the appropriateness of the learning model for the data domain. This is also applicable to deep learning models for automatic product classification. In this study, we propose deep learning models that are conscious of input data comprising text-based product information. Our approaches exploit two well-known deep learning models and integrate them with the processes of input data selection, transformation, and filtering. We demonstrate the practicality of these models through experiments using actual product information data. The experimental results show that the models that systematically consider the input data may differ in accuracy by approximately 30% from those that do not. This study indicates that input data should be sufficiently considered in the development of deep learning models for product classification.
Funder
National Research Foundation of Korea
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference23 articles.
1. Product categorization with LSTMs and balanced pooling views;Skinner,2018
2. Large Scale Product Categorization using Structured and Unstructured Attributes;Krishnan;arXiv,2019
Cited by
3 articles.
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