Intelligent tourism itinerary generation through natural language processing and hybrid recommendation systems

Abstract

Traditional travel planning systems rely on rigid formbased interfaces with predefined dropdown menus, limiting user’s ability to express nuanced preferences. This constraint often results in generic itineraries that fail to capture individual travel styles, budgets, or interests. To address this gap, we developed TravelMate AI, a commercial itinerary builder that introduces two key innovations: natural language processing (NLP) for interpreting free-text trip descriptions and a hybrid recommendation system combining machine learning with large language models (LLMs). The novelty lies in enabling users to describe their travel preferences conversationally (e.g., “Cultural tour of Kandy with family, medium budget”) while maintaining structured outputs suitable for commercial deployment. By eliminating the need for form-based inputs, the system democratizes travel planning for non-technical users while retaining the precision of AI-driven recommendations. Furthermore, unlike existing commercial solutions, which either rely on structured inputs (TripAdvisor) or quizbased preference gathering (MindTrip), TravelMate AI processes unstructured text directly, enabling richer and more flexible itinerary customization. This adaptability allows users to input diverse and complex preferences without rigid constraints, making travel planning more personalized and user-friendly.