Explainable AI in E-Commerce: Seller Recommendations with Ethnocentric Transparency

Abstract

Personalized seller recommendations are fundamental to enhancing user experiences and increasing sales in e-commerce platforms. Traditional recommendation systems, however, often function as black-box models, offering limited interpretability. This paper explores the integration of Explainable AI (XAI) techniques, particularly Integrated Gradients (IG) and DeepLIFT (Deep Learning Important FeaTures), into a hybrid recommendation system. The proposed approach combines Matrix Factorization (MF) and Graph Neural Networks (GNNs) to deliver personalized and interpretable seller recommendations. Using real-world e-commerce datasets, the study evaluates how different features, such as user interaction history, social connections, and seller reputation contribute to recommendation outcomes. The system addresses the trade-offs between recommendation accuracy and interpretability, ensuring that insights are both actionable and trustworthy. Experimental results demonstrate that the hybrid model achieves substantial improvements in precision, recall, and F1-score compared to standalone MF and GNN-based approaches. Moreover, Integrated Gradients and DeepLIFT provide users with clear and intuitive explanations of the recommendation process, fostering trust in the system. This paper also introduces a comprehensive feature attribution analysis to quantify the impact of key factors, including behavioral patterns and network influence, on recommendation decisions. A comparative evaluation with state-of-the-art neural recommendation models highlights the effectiveness of the proposed system in balancing performance with interpretability. Finally, the study discusses future enhancements, such as incorporating explainability techniques tailored for multimodal data, employing reinforcement learning for adaptive personalization, and extending the model to handle dynamic user preferences. These findings underscore the importance of transparent, user-focused AI in driving innovation in e-commerce recommendation systems.