“Travel and Tourism Recommendation System Using Machine Learning.”

Abstract: The rapid expansion of online travel platforms has created a demand for intelligent recommendation systems capable of assisting travelers in selecting destinations, attractions, and activities that match their preferences. Traditional approaches, such as collaborative filtering and content-based filtering, each have significant limitations when applied in the tourism domain. Collaborative filtering often struggles with data sparsity and cold-start scenarios, while content-based filtering can result in overspecialization and reduced diversity of recommendations. These challenges highlight the need for a more robust solution that leverages the strengths of both approaches. To address this gap, we propose an adaptive hybrid travel and tourism recommendation system that integrates collaborative filtering using Singular Value Decomposition (SVD) with content-based methods based on textual and categorical attributes of destinations. A weighted fusion strategy is introduced to balance personalization with contextual relevance, thereby improving both recommendation accuracy and diversity. The system is evaluated using a synthesized dataset of tourist attractions and user ratings, with results showing that the hybrid approach significantly outperforms standalone models in terms of Precision@K, Recall@K, and NDCG@K. This research demonstrates the potential of hybridization for developing scalable, context-aware tourism recommender systems and offers a practical framework for deployment in real-world travel platforms.