K-Means Clustering-based Recommendation System for Enhancing Village Folk Tourism

Village folk tourism is of vital importance for cultural heritage protection and local economic development. Nonetheless, tourists have difficulties finding suitable attractions because the information is inadequately structured and recommendation processes are inefficient. Classical models normally depend on collaborative filtering or user tastes, which do not classify attractions according to cultural and geographic factors. In order to overcome such challenges, we suggest a K-Means clustering algorithm-based recommendation system. This system classifies village tourist destinations based on major attributes like cultural importance, interaction with tourists, and proximity. Using inputs such as social media popularity, tourist ratings, and geographical location, our model creates optimized clusters that offer more personalized and precise recommendations. This clustering method allows travelers to browse locations that more closely align with their interests, enhancing overall satisfaction. The experimental results show that our method effectively enhances recommendation accuracy. By reducing redundancy and providing more relevant recommendations, the model enhances the tourist experience while facilitating the preservation of cultural heritage. Furthermore, the scalability of the system in processing large datasets guarantees applicability. Future research will delve into combining deep learning methods to enable the recommendation system to respond more sensitively to dynamic tourist activities, seasonal patterns, and evolving visitor tastes, further developing the discipline of tourism recommendation systems.