Optimizing E-Commerce with Ensemble Learning and Iterative Clustering for Superior Product Selection

Yuchen Liu, M. Wang, Gangmin Li, Terry Payne, Yong Yue, Ka Lok Man

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Article number 10
Pages (from-to)2818-2839
Number of pages21
JournalKSII Transactions on Internet and Information Systems
Volume18
Issue number10
DOIs
Publication statusPublished - 31 Oct 2024

Keywords

  • E-commerce Sales Forecasting
  • Product Selection Strategy
  • Ensemble Machine Learning Models
  • Iterative Clustering Analysis
  • Product Attribute Extraction

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