Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Journal of Future Artificial Intelligence and Technologies
Adamu, Suleiman
Unknown Affiliation
Computer Science & IT

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Exploring Explainability in Multi-Category Electronic Markets: A Comparison of Machine Learning and Deep Learning Approaches Adamu, Suleiman; Iorliam, Aamo; Asilkan, Özcan
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 4 (2025): March 2025
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.3048-3719-58

Abstract

Artificial intelligence can change many industries as a global phenomenon. Over the years, this transformation has supported Electronic Markets in reengineering the processes and activities that take place in traditional markets, focusing on improving transaction effectiveness and efficiency. While our dependence on intelligent machines continues to grow, the demand for more transparent and interpretable models equally grows. Thus, explanations for machine decisions and predictions are needed to justify their reliability, which requires greater interpretability and often elaborates the need to understand the algorithms' underlying mechanism. This paper, therefore, proposed models based on Decision Tree (DT), Long Short-Term Memory (LSTM), and an ensemble of the two aforementioned models for improving CLV accuracy, interpretability, and explainability of AI-based models in the multi-category electronic market. An open-source e-commerce Behavior Data from a multi-category store, previously used by similar studies on XAI and CLV, was used in this experiment, ensuring the robustness of the product prediction and explanations and fair comparison. From the results, the models from this study demonstrated remarkable performance in terms of minimal error rates of MAE, MSE, and RMSE, with LSTM outperforming the other models. Regarding explainability and interpretation, the begin_time is ranked as the most relevant feature in CLV prediction.
Co-Authors Asilkan, Ozcan Iorliam, Aamo