A Fusion of Recommendation Techniques to Deliver Personalized Tourism Experience

Tourism is a global industry that increasingly relies on web‐based applications to provide tourists with information about destinations, routes, food, accommodation, and transport facilities. To enhance the personalized experience for tourists using these platforms, incorporating a recommendation component is essential. However, tourism recommendations face significant challenges, particularly data sparsity and the cold start problem, which can negatively impact the accuracy of suggestions. This study introduces an innovative hybrid recommender system designed to deliver personalized travel experiences. To address the issues of data sparsity and cold start, the proposed recommender system employed a combination of several filtering techniques. The fusion of these techniques improves user satisfaction by providing accurate and diverse travel recommendations. The proposed system has been specifically designed for the local tourism landscape. The dataset utilized in this study was collected from TripAdvisor to evaluate the system’s performance. The results indicate that the hybrid recommender system achieves high accuracy, with an accuracy rate of 90.71%. Compared to previous studies, the proposed approach significantly improves the delivery of personalized travel recommendations. The findings highlight the effectiveness of combining multiple filtering techniques to generate precise and diverse suggestions tailored to user preferences.