A Hybrid Intelligent Tourism Recommendation System Using Big Data and AI for Enhanced User-Centric Suggestions

This study focuses on developing an advanced tourism destination recommendation system combining big data and artificial intelligence (AI). AI is one of the most advanced technologies in various applications, such as healthcare, industry and various intelligent applications. This study focused on tourism, which helps guide tourist attractions according to the users’ interests. However, reality data are commonly insufficient, and specific traditional measures are not able to be figured out due to the absence of users’ shared rating items. Furthermore, user preferences are ignored by conventional filtering techniques, which leads to a notable drop in suggestion accuracy. This study presents an advanced solution called HITRS — A Hybrid Intelligent Tourism Recommendation System to address these issues. The proposed system combines the strength of collaborative and content-based filtering techniques to provide an accurate recommendation system regarding users’ interests. Here, collaborative filtering is improved with the Jeffries–Matusita Distance (JMSD) algorithm to find similarities between user interests based on their ratings and preferences. At the same time, content-based filtering uses machine learning methods more effectively, such as Google Cloud Vision API, to filter related information from user-generated content such as social media like Instagram pics. Here, the Term Frequency-Inverse Document Frequency (TF-IDF) approach is used to direct these properties. The Cosine Similarity Index is used to compare these properties with the features of tourist attractions. Additionally, the proposed HITRS performs large data volumes from various sources like social media, geolocation and search history to create complete user profiles. Real-time data processing ensures accurate suggestions and the latest trends. The system’s architecture is built to manage big data details, providing essential patterns and information. Through its combined advantages, the hybrid approach improves suggestion accuracy and overcomes the drawbacks of individual filtering techniques. The user-friendly environment of the proposed system allows travelers to communicate easily with the system, providing a simple and pleasurable search for new places. This solution can transform the tourism sector completely by delivering smart, data-driven suggestions regarding every traveler’s desires.