Product collaborative filtering based recommendation systems for large-scale E-commerce - Trinh, Trang; Nguyen, Van-Ho; Nguyen, Nghia; Nguyen, Duy-Nghia (International journal of information management data insights, 2025)
•E-commerce demands multi-choice products, challenging businesses.•Recommender systems reshape E-commerce with personalized experiences.•Scalability is a pressing issue for recommendation systems.•Parallel techniques tackle scalability challenges in E-commerce.•Apache Spark accelerates training time for large-scale E-commerce. 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.