A Personalized Hybrid Tourist Destination Recommendation System: An Integration of Emotion and Sentiment Approach
Issued Date
2024-01-01
Resource Type
ISSN
2158107X
eISSN
21565570
Scopus ID
2-s2.0-85202701512
Journal Title
International Journal of Advanced Computer Science and Applications
Volume
15
Issue
8
Start Page
18
End Page
27
Rights Holder(s)
SCOPUS
Bibliographic Citation
International Journal of Advanced Computer Science and Applications Vol.15 No.8 (2024) , 18-27
Suggested Citation
Chanrueang S., Thammaboosadee S., Yu H. A Personalized Hybrid Tourist Destination Recommendation System: An Integration of Emotion and Sentiment Approach. International Journal of Advanced Computer Science and Applications Vol.15 No.8 (2024) , 18-27. 27. doi:10.14569/IJACSA.2024.0150803 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/101102
Title
A Personalized Hybrid Tourist Destination Recommendation System: An Integration of Emotion and Sentiment Approach
Author(s)
Author's Affiliation
Corresponding Author(s)
Other Contributor(s)
Abstract
This research introduces a personalized hybrid tourist destination recommendation system tailored for the growing trend of independent travel, which leverages social media data for trip planning. The system sets itself apart from traditional models by incorporating both emotional and sentiment data from social platforms to create customized travel experiences. The proposed approach utilizes Machine Learning techniques to improve recommendation accuracy, employing Collaborative Filtering for emotional pattern recognition and Content-based Filtering for sentiment-driven destination analysis. This integration results in a sophisticated weighted hybrid model that effectively balances the strengths of both filtering techniques. Empirical evaluations produced RMSE, MAE, and MSE scores of 0.301, 0.317, and 0.311, respectively, indicating the system’s superior performance in predicting user preferences and interpreting emotional data. These findings highlight a significant advancement over previous recommendation systems, demonstrating how the integration of emotional and sentiment analysis can not only improve accuracy but also enhance user satisfaction by providing more personalized and contextually relevant travel suggestions. Furthermore, this study underscores the broader implications of such analysis in various industries, opening new avenues for future research and practical implementation in fields where personalized recommendations are crucial for enhancing user experience and engagement.