Citation

Lew, Sook Ling and Tang Weiling, Claireta and Lai, Zhi Ming (2025) Big Data-Driven Analytics and Ensemble Machine Learning for Behavior Prediction and Service Quality Enhancement. In: 2025 IEEE International Conference on Computing, ICOCO 2025, 6 October 2025 - 8 October 2025, Kuching, Malaysia.

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Abstract

This study looks at how big data analytics and machine learning (ML) can improve the tourism sector. It focuses on understanding tourist behavior and enhancing service quality. Traditional methods often lack access to rich information. This research shows how data techniques can help fill those gaps. It reviews both the strengths and weaknesses of using big data. It also addresses ethical and privacy concerns in handling tourist data. The main objectives are to: (1) analyze tourist behavior using big data tools, (2) examine how big data drives innovation in tourism, and (3) explore ethical and privacy issues related to data use. This study uses stacking ensemble learning method to improve prediction accuracy. This study also tests Random Forest (RF) and Support Vector Machine (SVM) for a dataset of hotel satisfaction scores in Europe. Furthermore, this study compares stacking with traditional models. With the findings from this study, trends in tourist preferences and satisfaction are revealed for improving services, experiences and innovation.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Big data, tourism, hotel satisfaction, machine learning, support vector machine
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	20 Apr 2026 03:51
Last Modified:	20 Apr 2026 04:02
URII:	http://shdl.mmu.edu.my/id/eprint/15779

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