Mobile Tourism Recommender System for Users to Get a Better Choice of Tour
DOI:
https://doi.org/10.31185/wjcms.186Keywords:
Recommendation system, tourism, filtering, collaborativeAbstract
The system might include a turn-by-turn route highlight to prevent fake preferences that check if the user has taken the course. A larger customer overview with more participants is required to acquire more insightful client feedback. Our ex-amination was designed as a lab experiment to gather initial data straight absent. While making fun of other clients and their system comments, we looked at a few initial objective mixtures. Doing field research with actual clients using our suggested model in real-world situations (such as when looking for a course online to work from home) is crucial. This will help us better understand how effective our approach is. In this article, we developed a creative method for recommending multimodal travel routes. In a client survey with 20 participants, we evaluated the applicability of our cross-breed computation and its usability. The results show that CF, in-formation-based, and well-liked course concepts complement one more successfully than cutting-edge course organizer advances. Thanks to the Google Guides Programming interface, our application can give seven different elective trip options.
References
Y. Gholap, “Study of tourism recommendation system,” Application of Communication Computational Intelligence and Learning, Routledge, 2023, pp. 146–156.
Y. Hu, “Two-Stage Tour Route Recommendation Approach by Integrating Crowd Dynamics Derived from Mobile Tracking Data,” Applied Sciences, vol. 13, no. 1, pp. 596–596, 2023.
D. Sharma Analysis of Personalized Tourism Recommender Systems, 2023.
L. Yu, “Collaborative group embedding and decision aggregation based on the attentive influence of individual members: A group recommendation perspective,” Decision Support Systems, vol. 165, pp. 113894–113894, 2023.
Y. Ge, H. Qi, and W. Qu, “The factors impacting the use of navigation systems: A study based on the technology acceptance model,” Transportation Research Part F: Traffic Psychology and Behaviour, vol. 93, pp. 106–117, 2023.
K. Kittipimpanon, “Use of and Satisfaction With Mobile Health Education During the COVID-19 Pandemic in Thailand: Cross-sectional Study,” JMIR Formative Research, vol. 7, pp. 43639–43639, 2023.
A. Sinha, “AI-Assisted Big Data Analytics for Smart Healthcare Systems,” Intelligent Internet of Things for Smart Healthcare Systems, pp. 81–81, 2023.
T. M. Jung and I. Joe, “An AI-Based Platform Architecture with Situational Awareness for Travel Plans,” Software Engineering Application in Systems Design: Proceedings of 6th Computational Methods in Systems and Software 2022, vol. 1, pp. 376–384, 2023.
J. Zhong and T. Chen, “Antecedents of mobile payment loyalty: An extended perspective of perceived value and information system success model,” Journal of Retailing and Consumer Services, vol. 72, pp. 103267–103267, 2023.
Y. Hu, “Two-Stage Tour Route Recommendation Approach by Integrating Crowd Dynamics Derived from Mobile Tracking Data,” Applied Sciences, vol. 13, no. 1, pp. 596–596, 2023.
J. M. Lee and J. K. Kim, “Effects of service quality of airline mobile application and individual characteristics on user satisfaction and intention to reuse,” International Journal of Mobile Communications, vol. 21, no. 1, pp. 134–157, 2023
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Copyright (c) 2023 Mostafa. M.khater, El-Sayed M. El-kenawy, Mostafa Abotaleb
This work is licensed under a Creative Commons Attribution 4.0 International License.
