Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms-Reference-Cited by-同舟云学术

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

Published:2023-12-31 Issue:1 Volume:3 Page:367-377
ISSN:2980-020X
Container-title:Orclever Proceedings of Research and Development
language:
Short-container-title:OPRD

Author:

Oskooei Amirkia Rafiei^ORCID,Terim Asli^ORCID,Arık Cemal^ORCID,Bıçakçı Engin^ORCID

Abstract

This article outlines the design and development process of an automatic product categorization software intended for use in e-commerce and online marketplace platforms. The project aims to tackle the urgent issue of effectively classifying a wide range of products within digital markets. By utilizing artificial intelligence (AI) and machine learning methodologies, the software effectively analyzes product descriptions, enabling users to seamlessly incorporate products through automated categorization. The significance of the project is rooted in its ability to guarantee the accuracy and consistency of category hierarchies, automate the process of categorizing, and improve the overall user experience. The main goals involve the process of project planning, data collecting, and preparation. Machine learning models have been built and subsequently incorporated to facilitate the study of product descriptions. Through strict evaluation and optimization processes, a high level of accuracy and efficiency is achieved, resulting in several anticipated benefits. These benefits cover automated product categorization, enhanced user experience, and the potential for online platforms to gain a competitive edge. The key elements of innovation involve AI-driven textual analysis, learning methodologies grounded in data, and the ability to adapt to diverse industry contexts. Precautions and backup strategies are implemented to tackle technical issues, including the selection of machine learning libraries and algorithms, ensuring data quality, and integrating with various platforms. The success criteria encompass the objective of achieving a minimum prediction accuracy rate of 90%, optimizing business efficiency, enhancing user pleasure, and ensuring smooth system functioning. This project is a significant contribution to the field of product categorization inside the digital marketplace, as it provides automation, accuracy, and efficiency, ultimately resulting in an enhanced user experience.

Publisher

Orclever Science and Research Group

Reference21 articles.

1. Lin, Yiu-Chang, et al. "A dataset and baselines for e-commerce product categorization." Proceedings of the 2019 ACM SIGIR international conference on theory of information retrieval. 2019.

2. Pan, Hong, and Hanxun Zhou. "Study on convolutional neural network and its application in data mining and sales forecasting for E-commerce." Electronic Commerce Research 20 (2020): 297-320.

3. Cao, Zhihao, Shaomin Mu, and Mengping Dong. "Two-attribute e-commerce image classification based on a convolutional neural network." The Visual Computer 36 (2020): 1619-1634.

4. Kozareva, Zornitsa. "Everyone likes shopping! multi-class product categorization for e-commerce." Proceedings of the 2015 conference of the North American chapter of the association for computational linguistics: human language technologies. 2015.

5. Somvanshi, Madan, et al. "A review of machine learning techniques using decision tree and support vector machine." 2016 international conference on computing communication control and automation (ICCUBEA). IEEE, 2016.