Implementation of forward chaining in tourism recommendation selection expert system based on user preferences
DOI:
https://doi.org/10.35335/mandiri.v12i2.247Keywords:
Expert System, Forward Chaining, Travel Recommendations, User Preferences, User PrivacyAbstract
Precise travel recommendations tailored to user preferences are a key element in enriching the travel experience. In this context, the Forward Chaining method in the User Preference-Based Travel Recommendation Selection Expert System offers a powerful and highly personalised approach. This article describes the implementation steps of the Forward Chaining method, which involves identification of user preferences, conversion of preferences to facts, initialisation of knowledge base, Forward Chaining process, evaluation of results, and customised travel recommendations. This method allows the system to dynamically respond to user preferences, generate accurate recommendations, and ensure that users are satisfied with their experience. However, some challenges such as privacy protection, complexity of user preferences, and knowledge base updates must be considered. Therefore, this article also discusses important implications of implementing the Forward Chaining method, including strict privacy protection, regular updates of the knowledge base, as well as the system's ability to learn from user feedback. In conclusion, the Forward Chaining method is a very useful approach in improving travel recommendation services tailored to user preferences, which can enrich the travel experience and result in higher satisfaction for users.
References
Ackerman, M. S., Cranor, L. F., & Reagle, J. (1999). Privacy in e-commerce: examining user scenarios and privacy preferences. Proceedings of the 1st ACM Conference on Electronic Commerce, 1–8.
Al-Ghuribi, S. M., & Noah, S. A. M. (2019). Multi-criteria review-based recommender system–the state of the art. IEEE Access, 7, 169446–169468.
Alemu, T. A., Tegegne, A. K., & Tarekegn, A. N. (2017). Recommender system in tourism using case based reasoning approach. International Journal of Information Engineering and Electronic Business, 9(5), 34.
Buckley, R., & Cooper, M.-A. (2021). Assortative matching of tourists and destinations: Agents or algorithms? Sustainability, 13(4), 1987.
Buhalis, D., & Amaranggana, A. (2015). Smart tourism destinations enhancing tourism experience through personalisation of services. Information and Communication Technologies in Tourism 2015: Proceedings of the International Conference in Lugano, Switzerland, February 3-6, 2015, 377–389.
Chen, J. S., & Gursoy, D. (2001). An investigation of tourists’ destination loyalty and preferences. International Journal of Contemporary Hospitality Management, 13(2), 79–85.
Cheng, W., Tian, R., & Chiu, D. K. W. (2023). Travel vlogs influencing tourist decisions: information preferences and gender differences. Aslib Journal of Information Management.
Coles, A., Coles, A., Fox, M., & Long, D. (2010). Forward-chaining partial-order planning. Proceedings of the International Conference on Automated Planning and Scheduling, 20, 42–49.
Dolnicar, S. (2022). Market segmentation for e-tourism. In Handbook of e-Tourism (pp. 849–863). Springer.
Fink, P. K., Lusth, J. C., & Duran, J. W. (1985). A general expert system design for diagnostic problem solving. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5, 553–560.
Grossmann, W., Sertkan, M., Neidhardt, J., & Werthner, H. (2019). Pictures as a tool for matching tourist preferences with destinations. Personalized Human-Computer Interaction, 1–5.
Halkiopoulos, C., Antonopoulou, H., Gkintoni, E., & Giannoukou, I. (2021). An expert system for recommendation tourist destinations: An innovative approach of digital marketing and decision-making process. International Journal of Innovative Science and Research Technology, 6(4), 398–404.
Hayes-Roth, F. (1984). The knowledge-based expert system: A tutorial. Computer, 17(09), 11–28.
Islam, M. R., Abdul Kader Jilani, M. M., Miah, S. J., Akter, S., & Ulhaq, A. (2021). Discovering tourist preference for electing destinations: a pattern mining based approach. Asia Pacific Journal of Tourism Research, 26(10), 1081–1096.
Lucas, J. P., Luz, N., Moreno, M. N., Anacleto, R., Figueiredo, A. A., & Martins, C. (2013). A hybrid recommendation approach for a tourism system. Expert Systems with Applications, 40(9), 3532–3550.
Maher, M. Lou. (1986). Problem solving using expert system techniques.
Manurung, J., Perwira, Y., & Sinaga, B. (2022). Expert System to Diagnose Dental and Oral Disease Using Naive Bayes Method. 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM), 1–4.
Manurung, J., Ramen, S., & Logaraj, L. (2023). Clustering method for predicting campaign results based on voter and candidate characteristics. Jurnal Mantik, 7(2), 1402–1408.
Moutinho, L., Rate, S., & Ballantyne, R. (2013). 22 Futurecast: An Exploration of Key Emerging Megatrends in the Tourism Arena. Trends in European Tourism Planning and Organisation, 60.
Pizam, A., & Mansfeld, Y. (1999). Consumer behavior in travel and tourism. Psychology Press.
Rehman Khan, H. U., Lim, C. K., Ahmed, M. F., Tan, K. L., & Bin Mokhtar, M. (2021). Systematic review of contextual suggestion and recommendation systems for sustainable e-tourism. Sustainability, 13(15), 8141.
Roque, V., & Raposo, R. (2016). Looking for Tourism Related Information in the Social Media Landscape: an Analysis of Portuguese Tourists’ Habits. 3rd European Conference on Social M Di R h Media Research EM Normandie, Caen, France, 349.
Sanchez, O. R., Torre, I., He, Y., & Knijnenburg, B. P. (2020). A recommendation approach for user privacy preferences in the fitness domain. User Modeling and User-Adapted Interaction, 30, 513–565.
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