KSII Transactions on Internet and Information Systems (TIIS)

  • 제18권10호
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  • Pages.2818-2839
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  • 2024
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Optimizing E-Commerce with Ensemble Learning and Iterative Clustering for Superior Product Selection

  • Yuchen Liu (Department of Computing, Xi'an Jiaotong-Liverpool University) ;
  • Meng Wang (Department of Computing, Xi'an Jiaotong-Liverpool University) ;
  • Gangmin Li (HeXie Management Research Centre, College of Industry-Entrepreneurs (CIE), Xi'an Jiaotong-Liverpool University) ;
  • Terry R. Payne (Department of Computer Science, University of Liverpool) ;
  • Yong Yue (Department of Computing, Xi'an Jiaotong-Liverpool University) ;
  • Ka Lok Man (Department of Computing, Xi'an Jiaotong-Liverpool University)
  • 투고 : 2024.02.21
  • 심사 : 2024.09.26
  • 발행 : 2024.10.31
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초록

With the continuous growth of e-commerce sales, a robust product selection model is essential to maintain competitiveness and meet consumer demand. Current research primarily focuses on single models for sales prediction and lacks an integrated approach to sales forecasting and product selection. This paper proposes a comprehensive framework (VN-CPC) that combines sales forecasting with product selection to address these issues. We integrate a series of classical machine learning models, including Tree Models (XGBoost, LightGBM, CatBoost), Support Vector Machine (SVM), Bayesian Ridge, and Artificial Neural Networks (ANN), using a voting mechanism to determine the optimal weighting scheme. Our method demonstrates a lower Root Mean Square Error (RMSE) on collected Amazon data than individual models and other ensemble models. Furthermore, we employ a three-tiered clustering model: Initial Clustering, Refinement Clustering, and Final Clustering, based on our predictive model to refine product selection to specific categories. This integrated forecasting and selection framework can be more effectively applied in the dynamic e-commerce environment. It provides a robust tool for businesses to optimize their product offerings and stay ahead in a competitive market.

키워드

과제정보

This work is partially supported by the XJTLU AI University Research Centre and Jiangsu Province Engineering Research Centre of Data Science and Cognitive Computation at XJTLU and SIP AI innovation platform (YZCXPT2022103). Also, it is partially funded by the Suzhou Municipal Key Laboratory for Intelligent Virtual Engineering (SZS2022004) as well as funding: XJTLU Key Program Special Fund (KSF-A-17).

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