Hybrid Recommender System for Tourism Based on Big Data and AI: A Conceptual Framework

The rapid growth of digital tourism platforms and user-generated content has created an urgent need for intelligent systems that can deliver personalized travel recommendations. This paper proposes a conceptual framework for a Hybrid Recommender System for Tourism that leverages Big Data analytics and Artificial Intelligence to enhance travel planning and decision-making. The framework integrates multiple recommendation techniques—collaborative filtering, content- based filtering, and context-aware methods—to overcome the limitations of single- approach systems such as cold-start problems, data sparsity, and lack of personalization. Machine learning and deep learning models are incorporated to identify complex patterns in user behavior, while Big Data technologies enable scalable processing and real-time responsiveness.