Product collaborative filtering based recommendation systems for large-scale E-commerce

作     者：Trinh, Trang Nguyen, Van-Ho Nguyen, Nghia Nguyen, Duy-Nghia 

作者机构：University of Economics and Law Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam 

出 版 物：《International Journal of Information Management Data Insights》 (Int. J. Inf. Manag. Data Insights)

年 卷 期：2025年第5卷第1期

基　　金：Trường Đại học Kinh tế - Luật  Đại học Quốc gia Thành phố Hồ Chí Minh  VNUHCM-UEL 

主　　题：Apache spark Collaborative filtering E-commerce Large-scale Parallel and distributed computing Recommendation systems 

摘      要：The rapid growth in e-commerce and the increasing diversity of customer preferences necessitates the development of an effective recommender system for a business offering a wide range of products. This paper introduces a product-based collaborative filtering approach utilizing Apache Spark, a powerful parallel processing framework to address the scalability issues of recommender systems in the cloud computing environment. Using Spark s distributed computing ability, our model attains a surprising 7.6 times speedup on the training time compared to traditional single-machine methods while preserving accuracy with a Root Mean Square Error (RMSE) 0.9. These results demonstrate the effectiveness of parallel and distributed techniques in developing efficient and accurate recommender systems for large-scale e-commerce applications. Future work will focus on applying multi-model to enhance the accuracy of prediction and configuration to optimize the cost of cluster operations. © 2025 The Author(s)