A hybrid tourism recommendation framework based on optical image recognition and collaborative filtering

As smart cities and tourism industries continue to evolve, personalized recommendation systems have become essential for meeting the diverse and dynamic preferences of users. This study proposes a hybrid tourism recommendation framework that integrates optical image recognition with collaborative filtering (CF), addressing challenges such as data sparsity, cold-start issues, and the lack of contextual understanding. By leveraging optical technologies, such as drone-captured aerial imagery and optical character recognition (OCR), the framework analyzes real-time scenic area information, including crowd density and weather conditions, while enhancing user scene comprehension through the recognition of maps, road signs, and menus. Additionally, the framework constructs a domain-specific tourism knowledge graph to model destinations, activities, and user preferences as multi-relational data. Advanced embedding techniques, such as TransE and RotatE, are applied to extract meaningful semantic relationships, which are incorporated into the CF model to enrich the user-item interaction matrix with contextual information. Extensive experiments conducted on real-world datasets demonstrate that the proposed framework significantly outperforms traditional CF and existing hybrid models, achieving notable improvements in Precision@k, Recall@k, F1-score, and diversity. These results highlight the ability of the framework to generate highly personalized and context-aware recommendations, providing an innovative solution for advancing smart tourism systems and enhancing user satisfaction through accurate and diverse travel suggestions.