Understanding Customer Loyalty-Aware Recommender Systems in E-Commerce: an Analytical Perspective

Abstract

The selection of relevant variables is critical for providing personalized product and service recommendations on e-commerce businesses. However, the integration of e-loyalty-related features into recommender systems remains underexplored. This study aims to investigate the impact of incorporating e-loyalty indicators, such as purchase frequency and platform engagement, on the performance of recommender systems in the context of e-commerce businesses. Using three well-established recommender system models and four real-world datasets, we conducted computational experiments to assess performance improvements when e-loyalty features are incorporated. The results show that integrating e-loyalty-related features significantly enhances the performance of recommendation systems, with sequential deep neural networks outperforming other algorithms. Our study contributes to the literature by highlighting the value of leveraging customer loyalty data to enhance recommendation accuracy. Theoretical implications include underscoring the importance of using longitudinal user engagement data in recommender systems to move beyond static personalization toward adaptive, behavior-aware technologies. From a practical perspective, our findings suggest that incorporating e-loyalty features can improve recommendation accuracy, offering valuable insights for e-commerce businesses seeking to personalize their services. This research offers original contributions by focusing on the role of loyalty-driven features in improving recommender systems, an area that remains largely underexplored.