Published Paper Details:

One of the sectors that have undergone tremendous change by the addition of innovative technologies is the tourism industry, specifically the introduction of Machine Learning (ML). The study describes and tests the creation and application of an ML-driven tourism management and recommendation system. Using methods like collaborative filtering, clustering and natural language processing the system can derive the interests of the travelers and can optimize the itineraries. Besides, it mitigates dynamics of cold-start problems, data sparsity, and user behavior. The suggested ML-based tourism system will benefit the decision-making of both travelers and tourism agencies to ensure the effective allocation of resources, the enhanced customer satisfaction, and the intelligent development of tourism. The conducted experiments prove that ML models are much more accurate and engaging than traditional rule-based systems. The paper advances the so-called intelligent tourism as a rapidly developing research area with the scalable and adaptive real-time recommendation and planning framework.