Personalized Recommendation Intelligent Fuzzy Clustering Model for theTourism

Massoud Qasimi

doi:10.69996/jcai.2024024

Authors

Massoud Qasimi Assistant Professor, Department of Computer Engineering Institute of Science, Karadeniz Technical University, Turkey. Author

DOI:

https://doi.org/10.69996/jcai.2024024

Keywords:

Fuzzy Model, Recommendation System, Classification, Artificial Intelligence, Satisfaction Level

Abstract

Personalized recommendations in tourism have transformed the way travellers explore new destinations, offering tailored experiences that align with individual preferences and interests. As the travel industry increasingly harnesses data analytics and artificial intelligence, tourists can now receive customized suggestions that reflect their unique tastes, whether they seek adventure, relaxation, cultural immersion, or culinary exploration. This paper explores the implementation and effectiveness of intelligent tourism management strategies using fuzzy clustering and personalized recommendations. By analyzing various scenarios, including baseline, personalized recommendations, dynamic pricing, crisis management, and enhanced resource allocation, the study demonstrates how advanced data-driven techniques can significantly improve key performance indicators such as visitor satisfaction, total revenue, repeat visitation rates, and operational efficiency. The simulation results reveal that personalized recommendations and optimized resource allocation are particularly effective in enhancing visitor experiences and economic outcomes. Conversely, the analysis underscores the critical need for robust
crisis management strategies to maintain performance during adverse events. This research providesvaluable insights into the transformative potential of intelligent systems in modern tourism management, offering a pathway towards more resilient and competitive tourism destinations. For instance, personalized recommendations increased average visitor satisfaction from 7.2 to 8.5, total revenue from $500,000 to $600,000, and the repeat visitation rate from 35% to 45%. The dynamic pricing strategy improved visitor satisfaction to 7.8 and revenue to $550,000, while enhanced resource allocation resulted in a satisfaction rate of 7.9 and revenue of $570,000. Conversely, crisis management showed the importance of preparedness, as satisfaction dropped to 6.5 and revenue to $450,000. These results underscore the critical need for robust management strategies.

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