Citation

Nova, Afsana Alam and Nagib, Mir Nafiul and Pervez, Rahat and Hossen, Md. Jakir and Mridha, M. F. (2025) CustXaiNet: A Multi-Modal Deep Learning Framework for Predicting Customer Behavior With Explainable AI. IEEE Open Journal of the Computer Society. pp. 1-12. ISSN 2644-1268

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Abstract

This paper presents CustXaiNet, a novel multi-modal deep learning framework for predicting customer behavior in e-commerce applications using explainable AI. CustXaiNet integrates numerical transactional data and textual review data through hierarchical attention and temporal modeling, enabling robust predictions across three key tasks: purchase likelihood, basket size, and sentiment classification. The model demonstrates state-of-the-art performance, achieving an accuracy of 93.2% and an F1-score of 92.7% for purchase likelihood prediction, an RMSE of 8.9 and an R2-Score of 0.95 for basket size prediction, and an accuracy of 94.1% with an F1-score of 93.6% for sentiment classification. Furthermore, CustXaiNet incorporates explainability mechanisms using SHAP values and attention weights, achieving an average interpretability score of 0.91 across tasks. Comparative analysis with existing models, including BERT and LSTM, highlights CustXaiNet's superiority in both predictive performance and transparency. This work demonstrates the potential of explainable multi-modal AI in enhancing e-commerce analytics, enabling actionable insights for personalized marketing and operational efficiency. CustXaiNet not only achieves high predictive performance but also aligns with the principles of trustworthy and transparent AI by embedding interpretability at the core of its architecture.

Item Type:	Article
Uncontrolled Keywords:	Multi-modal deep learning, customer behavior prediction, explainable AI, E-Commerce
Subjects:	H Social Sciences > HF Commerce Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Nor Afiqah Mohd Adnan
Date Deposited:	06 Nov 2025 07:36
Last Modified:	07 Nov 2025 02:20
URII:	http://shdl.mmu.edu.my/id/eprint/14737

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