Hybrid Content-Based and Collaborative Filtering Recommender System for Tourist Destination Recommendation

Abstract

The rapid growth of digital tourism platforms has transformed how tourists search for travel information and select destinations. However, the abundance of available information often causes information overload, making it difficult for users to identify destinations that match their preferences. This study designs and implements a web-based tourist destination recommender system using a Hybrid Recommender System that combines Content-Based Filtering (CBF) and Collaborative Filtering (CF) to generate accurate and personalized recommendations. The CBF component applies TF-IDF and Cosine Similarity to analyze destination attributes, while the CF component predicts user preferences based on historical rating patterns. The recommendation scores from both methods are integrated using the Weighted Hybrid approach with a weighting parameter of α = 0.5. System performance was evaluated using the Precision@3 metric on a dataset containing 50 tourist destinations in Blora Regency, 50 users, and five tourism categories. The experimental results achieved an average Precision@3 score of 63.33%, indicating that approximately two of the top three recommended destinations were relevant to user preferences. These findings demonstrate that combining destination content with user rating behavior enhances recommendation quality compared with using a single recommendation technique. The proposed system can support tourists in making informed travel decisions through personalized and relevant destination recommendations while providing a practical approach for tourism recommendation applications.