Citation

Kumar, Viknesh and Yeo, Boon Chin and Lim, Way Soong (2024) Intelligent Tourist Attractions Recommender System with Hybrid Collaborative Filtering. In: 2024 Multimedia University Engineering Conference (MECON), 23-25 July 2024, Cyberjaya, Malaysia.

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Abstract

Annual rise in tourism increases demand for a robust tourism recommendation. Big data processing has been adopted as the main component of adaptive recommendation systems, which provide services that are designed to suit the needs of individual users. Big data processing can be applied to tourism recommendation to provide better options for tourists and reduce distance travelled by user in order to visit a location of their preference. In this system, Logistic Regression is used to categorize big data and recommend tourism types to suit the user's personality is devised based on sentiment analysis, distance and cost of visit. The system is scalable to handle the complexity of the data collected by the system. It informs the user locations with the lowest Mean Squared Error through cumulative values from different tourism criteria. The current best cumulative MSE achieved by the system is 9.482 for a training dataset of 438. The MSE is expected to improve with higher dataset for training.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Recommendation System, Big Data, Deep Learning, Sentiment Analysis
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	07 Feb 2025 03:50
Last Modified:	07 Feb 2025 03:50
URII:	http://shdl.mmu.edu.my/id/eprint/13406

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