Citation

Kumar, Viknesh and Yeo, Boon Chin and Lim, Way Soong (2022) Intelligent Tourist Attractions Recommender System With Hybrid Collaborative Filtering. In: 2nd FET PG Engineering Colloquium Proceedings 2022, 1-15 December 2022, Multimedia University, Malaysia. (Unpublished)

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Abstract

Background: Annual rise in tourism increases risk of carbon emission contributing to climate change. Purpose: 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 carbon footprint resulting from tourism. Methodology: Deep learning serves as a tool used to analyse big data for best outcome. In this proposal, an idea of a system that uses deep learning to categorize big data and recommend tourism types to suit the user's personality is devised. The proposed system will be scalable to handle the complexity of the data collected by the system. The proposed system will take advantage of hybrid collaborative filtering for data segmentation and analysis. It will also use estimation of emission rate, distance travelled and crowd monitoring to reduce carbon emission. Findings: The system will be able to inform the user about the available options based on their chosen personality type, tourism type and sentiment analysis of tourist reviews while ensuring sparsity and reducing distance travelled by travellers to ensure minimal carbon emission. Research Limitations: The limitations of this research will be based on the user’s choices. If the user is able to have more choices of location to visit, the system will have more data to train and analyse resulting in a more robust system. Originality/Value: The research will be unique to include carbon metrics within tourism recommendation system to combat carbon footprint and global warming.

Item Type:	Conference or Workshop Item (Other)
Uncontrolled Keywords:	Carbon Emission, Recommendation System, Big Data, Deep Learning, Sentiment Analysis
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	16 Feb 2023 05:03
Last Modified:	16 Feb 2023 05:03
URII:	http://shdl.mmu.edu.my/id/eprint/10814

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