Intelligent Destination Recommender and Community Builder
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Abstract
Recommendation engines make use of machine learning techniques and generally deal with ranking and rating of products/users. With the help of this system we aim to suggest different destinations to users based on their interest and previous visits. Along with recommendations we also aspire to enable users to build travel communities for people sharing similar interests .This shall help travelers with planning ,meeting like-minded people,safety and enthralling experience.
As per the analysis done on pre-existing systems we discerned that enabling users to build a community of travelers visiting the same destination is an eccentric attribute proposed.
This distinctive attribute of building communities shall be implemented using the basics of clustering algorithms in Machine learning.
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References
Delgado, J., & Davidson, R. (2002). Knowledge Bases and User Profiling in Travel and Hospitality Recommender Systems. Proceedings of the ENTER 2002 Conference, page 1-16. Springer Verlag
Fesenmaier, D. R., Ricci, F., Schaumlechner, E., Wöber, K., & Zanella, C. (2003). Dietorecs: Travel advisory for multiple decision styles. In Information and communication technologies in tourism 2003 (pp. 232- 241). Springer Vienna
Qureshi, D. I. ., & Patil, M. S. S. . (2022). Secure Sensor Node-Based Fusion by Authentication Protocol Using Internet of Things and Rfid. Research Journal of Computer Systems and Engineering, 3(1), 48–55. Retrieved from https://technicaljournals.org/RJCSE/index.php/journal/article/view/41.
Tourism Recommendation System: A Survey and Future Research Directions. (2022). Amrita Vishwa Vidyapeetham
Tourism recommendation system: a survey and future research directions. (2022). Multimedia Tools and Applications
Benjamin Jackson, Mark Johnson, Andrea Ricci, Piotr Wi?niewski, Laura Martínez. Ethical Considerations in Machine Learning Applications for Decision Science. Kuwait Journal of Machine Learning, 2(4). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/221
Algorithm of Apriori-Based Rural Tourism Driving Factors and Its System Optimization Method. (2022). Mobile Information Systems
Research on Tourism Service Intelligent Recommendation System Based on Apriori-MD Algorithm. (2015). Applied Mechanics and Materials
Using K-Means to Cluster Users and Recommend Similar Trip Destinations. (2021). Medium
Ahmed Abdelaziz, Machine Learning Approaches for Predicting Stock Market Volatility , Machine Learning Applications Conference Proceedings, Vol 3 2023.
Implementing K-Means Clustering Algorithm in Collaborative Trip Advisory and Planning System. (2019). ResearchGate