Leveraging Machine Learning for Personalized Recommendations in Mobile Tourism: A Study on Collaborative and Content-Based Filtering
DOI:
https://doi.org/10.51903/jtie.v3i2.190Keywords:
Machine Learning, Collaborative Filtering, Content-Based Filtering, Mobile Tourism Applications, Personalized RecommendationsAbstract
The rapid growth of the tourism industry, coupled with the increasing reliance on mobile technology, necessitates the development of intelligent applications that enhance user experience through personalized recommendations. This research investigates the application of machine learning techniques, specifically Collaborative Filtering and Content-Based Filtering, to create a mobile tourism application that addresses the challenges of providing relevant and tailored suggestions to users. The primary objective of this study is to improve user satisfaction and engagement by delivering precise recommendations based on individual preferences and behaviors. To achieve this, the research employs a mixed-methods approach, combining quantitative data analysis from user interactions and qualitative assessments of user feedback. The study utilizes datasets sourced from reputable platforms, including user ratings and demographic information, to train the recommendation algorithms effectively. The evaluation of the implemented system demonstrates a significant increase in recommendation accuracy, leading to enhanced user satisfaction and increased visitation rates to tourist attractions. The findings indicate that the integration of Collaborative Filtering and Content-Based Filtering not only optimizes the personalization of recommendations but also fosters user loyalty and engagement with the application. By leveraging machine learning techniques, tourism providers can better understand user preferences, ultimately leading to a competitive advantage in a crowded market. This study contributes to the ongoing discourse on the intersection of technology and tourism, offering valuable insights for future research and application development in this dynamic field.
References
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Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology and Tourism, 22(3), 455–476. https://doi.org/10.1007/S40558-019-00160-3/METRICS
Permana, K. E. (2024). Comparison of User Based and Item Based Collaborative Filtering in Restaurant Recommendation System. Mathematical Modelling of Engineering Problems, 11(7), 1922. https://doi.org/10.18280/MMEP.110723
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Stitini, O., Kaloun, S., & Bencharef, O. (2023). Towards a robust solution to mitigate all content-based filtering drawbacks within a recommendation system. International Journal of Systematic Innovation, 7(7), 89–111. https://doi.org/10.6977/IJOSI.202309_7(7).0006
Trichur Narayanan, R. (2021). Recommender System: Personalizing User Experience or Scientifically Deceiving Users? ACM International Conference Proceeding Series, 138–144. https://doi.org/10.1145/3471287.3471303
Wang, Y., Zhu, J., Liu, R., & Jiang, Y. (2024). Enhancing recommendation acceptance: Resolving the personalization–privacy paradox in recommender systems: A privacy calculus perspective. International Journal of Information Management, 76, 102755. https://doi.org/10.1016/J.IJINFOMGT.2024.102755
Wang, Z. (2023). Intelligent recommendation model of tourist places based on collaborative filtering and user preferences. Applied Artificial Intelligence, 37(1). https://doi.org/10.1080/08839514.2023.2203574
Widayanti, R., Chakim, M. H. R., Lukita, C., Rahardja, U., & Lutfiani, N. (2023). Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering. Journal of Applied Data Sciences, 4(3), 289–302. https://doi.org/10.47738/JADS.V4I3.115
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Yang, X., Zhang, L., & Feng, Z. (2023). Personalized Tourism Recommendations and the E-Tourism User Experience. Https://Doi.Org/10.1177/00472875231187332, 63(5), 1183–1200. https://doi.org/10.1177/00472875231187332
Zhang, B., & Sundar, S. S. (2019). Proactive vs. reactive personalization: Can customization of privacy enhance user experience? International Journal of Human-Computer Studies, 128, 86–99. https://doi.org/10.1016/J.IJHCS.2019.03.002
Agarwal, R., Sanghi, A., Agarwal, G., Saxena, S., & Kumar, R. (2024). Comparison of user-based and item-based collaborative filtering using similarity metrics. AIP Conference Proceedings, 3168(1). https://doi.org/10.1063/5.0217219/3300781
Aldayel, M., Al-Nafjan, A., Al-Nuwaiser, W. M., Alrehaili, G., & Alyahya, G. (2023). Collaborative Filtering-Based Recommendation Systems for Touristic Businesses, Attractions, and Destinations. Electronics 2023, Vol. 12, Page 4047, 12(19), 4047. https://doi.org/10.3390/ELECTRONICS12194047
Aljizawi, J., & Kafrawy, E. (2023). Personalized Travel Recommendations and Marketing Automation for Saudi Arabia: Harnessing AI for Enhanced User Experience and Business Growth. https://repository.effatuniversity.edu.sa/handle/20.500.14131/1502
Arabi, H. (2019). Collaborative And Content Based Filtering Personalized Recommender System For Book.
Buhalis, D., & Amaranggana, A. (2015). Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services. Information and Communication Technologies in Tourism 2015, 377–389. https://doi.org/10.1007/978-3-319-14343-9_28
Cena, F., Console, L., & Vernero, F. (2023). How to deal with negative preferences in recommender systems: a theoretical framework. Journal of Intelligent Information Systems, 60(1), 23–47. https://doi.org/10.1007/S10844-022-00705-9/FIGURES/2
Charlin, L., Zemel, R. S., & Larochelle, H. (2014). Leveraging user libraries to bootstrap collaborative filtering. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 173–182. https://doi.org/10.1145/2623330.2623663/SUPPL_FILE/P173-SIDEBYSIDE.MP4
Chobanov, V., Galev, T., Mussetta, M., Todo, H., Zhang, X., Zhang, Z., & Todo, Y. (2024). Dynamic User Tourism Interest Modeling through Domain Information Integration: A Hierarchical Approach. Electronics 2024, Vol. 13, Page 2970, 13(15), 2970. https://doi.org/10.3390/ELECTRONICS13152970
Glauber, R., & Loula, A. (2019). Collaborative Filtering vs. Content-Based Filtering: differences and similarities. https://arxiv.org/abs/1912.08932v1
Ko, H., Lee, S., Park, Y., & Choi, A. (2022). A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields. Electronics 2022, Vol. 11, Page 141, 11(1), 141. https://doi.org/10.3390/ELECTRONICS11010141
Kwon, K., & Kim, C. (2012). How to design personalization in a context of customer retention: Who personalizes what and to what extent? Electronic Commerce Research and Applications, 11(2), 101–116. https://doi.org/10.1016/J.ELERAP.2011.05.002
Li, Y., Liu, K., Satapathy, R., Wang, S., & Cambria, E. (2024). Recent Developments in Recommender Systems: A Survey [Review Article]. IEEE Computational Intelligence Magazine, 19(2), 78–95. https://doi.org/10.1109/MCI.2024.3363984
Ma, Y. M., Li, M. Y., & Cao, P. P. (2024). Improving customer satisfaction in the hotel industry by fusing multi-source user-generated content: An integration method based on the heuristic-systematic model and evidence theory. Applied Intelligence, 54(17), 8719–8744. https://doi.org/10.1007/S10489-024-05621-9/METRICS
McLean, G., Al-Nabhani, K., & Wilson, A. (2018). Developing a Mobile Applications Customer Experience Model (MACE)- Implications for Retailers. Journal of Business Research, 85, 325–336. https://doi.org/10.1016/J.JBUSRES.2018.01.018
Nasser, A. M., Bhagat, J., Agrawal, A., & Devadas, T. J. (2023). Mean-reversion based hybrid movie recommender system using collaborative and content-based filtering methods. https://doi.org/10.22271/maths.2023.v8.i3Sb.1012
Nautiyal, R., Polus, R., Tripathi, A., & Shaheer, I. (2023). “To use or not to use” - Mobile technology in nature-based tourism experience. Journal of Outdoor Recreation and Tourism, 43, 100667. https://doi.org/10.1016/J.JORT.2023.100667
Pang, Y., Hao, Q., Yuan, Y., Hu, T., Cai, R., & Zhang, L. (2011). Summarizing tourist destinations by mining user-generated travelogues and photos. Computer Vision and Image Understanding, 115(3), 352–363. https://doi.org/10.1016/J.CVIU.2010.10.010
Papadakis, H., Papagrigoriou, A., Panagiotakis, C., Kosmas, E., & Fragopoulou, P. (2022). Collaborative filtering recommender systems taxonomy. Knowledge and Information Systems, 64(1), 35–74. https://doi.org/10.1007/S10115-021-01628-7/METRICS
Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology and Tourism, 22(3), 455–476. https://doi.org/10.1007/S40558-019-00160-3/METRICS
Permana, K. E. (2024). Comparison of User Based and Item Based Collaborative Filtering in Restaurant Recommendation System. Mathematical Modelling of Engineering Problems, 11(7), 1922. https://doi.org/10.18280/MMEP.110723
Quijano-Sánchez, L., Cantador, I., Cortés-Cediel, M. E., & Gil, O. (2020). Recommender systems for smart cities. Information Systems, 92, 101545. https://doi.org/10.1016/J.IS.2020.101545
Raheem, M., Wong, C., & Harn, S. (2023). Recommendation System on Travel Destination based on Geotagged Data. Article in International Journal of Advanced Computer Science and Applications, 14(5), 2023. https://doi.org/10.14569/IJACSA.2023.0140511
Roobini, M. S., David, S. P., Lokeshwaran, S., Vinothini, E., & Aishwarya, D. (2024). Content Filtering Based Navigation Recommendation System Using NLP. Proceedings of 9th International Conference on Science, Technology, Engineering and Mathematics: The Role of Emerging Technologies in Digital Transformation, ICONSTEM 2024. https://doi.org/10.1109/ICONSTEM60960.2024.10568589
Son, J., & Kim, S. B. (2017). Content-based filtering for recommendation systems using multiattribute networks. Expert Systems with Applications, 89, 404–412. https://doi.org/10.1016/J.ESWA.2017.08.008
Stitini, O., Kaloun, S., & Bencharef, O. (2023). Towards a robust solution to mitigate all content-based filtering drawbacks within a recommendation system. International Journal of Systematic Innovation, 7(7), 89–111. https://doi.org/10.6977/IJOSI.202309_7(7).0006
Trichur Narayanan, R. (2021). Recommender System: Personalizing User Experience or Scientifically Deceiving Users? ACM International Conference Proceeding Series, 138–144. https://doi.org/10.1145/3471287.3471303
Wang, Y., Zhu, J., Liu, R., & Jiang, Y. (2024). Enhancing recommendation acceptance: Resolving the personalization–privacy paradox in recommender systems: A privacy calculus perspective. International Journal of Information Management, 76, 102755. https://doi.org/10.1016/J.IJINFOMGT.2024.102755
Wang, Z. (2023). Intelligent recommendation model of tourist places based on collaborative filtering and user preferences. Applied Artificial Intelligence, 37(1). https://doi.org/10.1080/08839514.2023.2203574
Widayanti, R., Chakim, M. H. R., Lukita, C., Rahardja, U., & Lutfiani, N. (2023). Improving Recommender Systems using Hybrid Techniques of Collaborative Filtering and Content-Based Filtering. Journal of Applied Data Sciences, 4(3), 289–302. https://doi.org/10.47738/JADS.V4I3.115
Xia, L., Baghaie, S., & Mohammad Sajadi, S. (2024). The digital economy: Challenges and opportunities in the new era of technology and electronic communications. Ain Shams Engineering Journal, 15(2), 102411. https://doi.org/10.1016/J.ASEJ.2023.102411
Yang, X., Zhang, L., & Feng, Z. (2023). Personalized Tourism Recommendations and the E-Tourism User Experience. Https://Doi.Org/10.1177/00472875231187332, 63(5), 1183–1200. https://doi.org/10.1177/00472875231187332
Zhang, B., & Sundar, S. S. (2019). Proactive vs. reactive personalization: Can customization of privacy enhance user experience? International Journal of Human-Computer Studies, 128, 86–99. https://doi.org/10.1016/J.IJHCS.2019.03.002
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2024-08-21
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Leveraging Machine Learning for Personalized Recommendations in Mobile Tourism: A Study on Collaborative and Content-Based Filtering. (2024). Journal of Technology Informatics and Engineering, 3(2), 235-248. https://doi.org/10.51903/jtie.v3i2.190
