<![CDATA[This study explores the application of Agglomerative Hierarchical Clustering (AHC) to categorize global cities based on thematic travel preferences, aiming to enhance personalized travel recommendations. The dataset used contains travel information for 560 cities worldwide, including thematic ratings across nine categories: culture, adventure, nature, beaches, nightlife, cuisine, wellness, urban, and seclusion, along with climate data and city descriptions. Feature engineering was performed to calculate an overall rating for each city by averaging its thematic scores, and to compute an average annual temperature from monthly climate data. The primary objective of this research was to use AHC to group cities into distinct clusters based on these thematic ratings. The analysis revealed six clusters, each representing different types of travel experiences. Cluster 1 consists of urban cultural hubs with high ratings for culture, cuisine, and urban experiences, while Cluster 2 features cities with a balance of cultural and culinary experiences alongside moderate natural and nightlife attractions. Cluster 3 represents remote, nature-focused cities with high ratings for seclusion and nature. Cluster 4 includes cities renowned for their beaches, nature, and cuisine, while Cluster 5 groups cities that emphasize adventure, nature, and seclusion. Cluster 6 is made up of destinations with a focus on nature, adventure, and seclusion, offering a balance between outdoor activities and tranquility. These findings offer a deeper understanding of the diversity in global city offerings and can significantly improve the effectiveness of travel recommendation systems by aligning cities with users' thematic preferences. By categorizing cities into meaningful clusters, personalized travel suggestions can be made based on users’ specific interests, such as cultural exploration, adventure, or nature. This research lays the groundwork for future studies to incorporate additional data sources and explore alternative clustering techniques for even more refined travel recommendations. The practical applications of this research can enhance real-world travel recommendation platforms, making them more tailored and relevant to individual user preferences]]>
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