Intelligent Tourist Attractions Recommender System with Hybrid Collaborative Filtering


Kumar, Viknesh and Yeo, Boon Chin and Lim, Way Soong (2023) Intelligent Tourist Attractions Recommender System with Hybrid Collaborative Filtering. In: 2nd FET PG Engineering Colloquium Proceedings 2023, 1-31 December 2023, Multimedia University, Malaysia. (Submitted)

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Annual rise in tourism increases increase 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. Deep learning serves as a tool used to analyze big data for best outcome. In this system, Logistic Regression is used to categorize big data and recommend tourism types to suit the user's personality is devised. The system is scalable to handle the complexity of the data collected by the system. It informs the user locations with the lowest Mean Absolute Error as options based on user’s preference type, tourism type and sentiment analysis of tourist reviews.

Item Type: Conference or Workshop Item (Poster)
Uncontrolled Keywords: Tourism, Hybrid, Deep Learning
Subjects: Q Science > QA Mathematics > QA71-90 Instruments and machines
Divisions: Faculty of Engineering and Technology (FET)
Depositing User: Ms Suzilawati Abu Samah
Date Deposited: 03 Apr 2024 02:15
Last Modified: 03 Apr 2024 02:15


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