Unveiling Hidden Patterns: Association Rule Mining in E-commerce Recommendation Systems

Abstract

In this paper, we explore the utilization of Association Rule Mining (ARM) in e-commerce recommendation systems. ARM has emerged as a powerful technique in discovering hidden relationships and patterns in transactional data, providing insights that can significantly enhance personalized user experiences in online platforms. This study investigates how association rules can be applied to e-commerce systems, examining the advantages, challenges, and impact on recommendation algorithms. The paper reviews the existing literature, presents a methodology for implementing ARM in e-commerce, and discusses the results of various experiments. A comparison of ARM with other common recommendation techniques is also provided, highlighting its unique contributions to e-commerce personalization.