Browsing by Author "Esmeli, Ramazan"

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An Analysis of Consumer Purchase Behavior Following Cart Addition in E-Commerce Utilizing Explainable Artificial Intelligence
(Mdpi, 2025) Esmeli, Ramazan; Gokce, Aytac
To optimize personalized offers and reduce cart abandonment, it is essential to understand customer behavior in e-commerce after products are added to the cart. Although purchase prediction models are well researched, session-level changes, including price variations, product category shifts, and geographical context, are less examined concerning their impact on machine learning models for predicting purchase behavior after cart additions. This study incorporates these factors into machine learning models to examine their impacts on predictions using explainable AI techniques. The comprehensive experimental results obtained from two datasets and eight models demonstrate that machine learning algorithms can achieve an F1 score of 89% in predicting purchase behavior following cart additions. This study highlights the significant impact of session-specific factors, like price fluctuations, category transitions, and geographical context, coupled with consumers' previous browsing patterns, on model predictions.
Session Context Data Integration To Address the Cold Start Problem in E-Commerce Recommender Systems
(Elsevier, 2024) Esmeli, Ramazan; Abdullahi, Hassana; Bader-El-Den, Mohamed; Can, Ali Selcuk
Recommender systems play an important role in identifying and filtering relevant products based on the behaviours of users. Nevertheless, recommender systems suffer from the 'cold-start' problem, which occurs when no prior information about a new session or a user is available. Many approaches to solving the cold-start problem have been presented in the literature. However, there is still room for improving the performance of recommender systems in the cold-start stage. In this article, we present a novel method to alleviate the cold-start problem in session-based recommender systems. The purpose of this work is to develop a session similarity-based cold-start session alleviation approach for recommendation systems. The developed method uses previous sessions' contextual and temporal features to find sessions similar to the newly started one. Our results on three different datasets show that, based on the provided Mean Average Precision and Normalised Discounted Cumulative Gain scores, the Session Similarity-based Framework consistently outperforms baseline models in terms of recommendation relevance and ranking quality across three used datasets. Our approach can be used to address the challenges associated with cold start sessions where no previously interacted items are present.
Understanding Customer Loyalty-Aware Recommender Systems in E-Commerce: an Analytical Perspective
(Springer, 2025) Esmeli, Ramazan; Can, Ali Selcuk; Awad, Aya; Bader-El-Den, Mohamed
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.