Integrating Reinforcement Learning and Natural Language Processing for Tourism Recommendation

In the modern period of AI supported smart tourism, it is the challenging task to offer personalized and circumstantially suitable travel suggestion with the abundance of destination-related data. This work offers a Integrating Reinforcement Learning and Natural Language Processing for Tourism Recommendation (RLNLP), that combines content-based and collaborative filtering along with Reinforcement Learning (RL) Model to surge recommendation flexibility and precision. The hybrid filtering component incorporates user preferences, past travel patterns, and destination features to generate a sorted list of recommendations. Reinforcement model the communicating behavior between the user and the system, also the user feedback is taken as a reward signal to frequently enhance the recommendation strategy. In-order to assess the sentiment data and textual reviews, improving the feature space and semantic thoughtful of user interest Natural Language Processing (NLP) will be used. Experimental results show that the proposed RL-NLP has improved performance over the traditional recommendation system in terms of accuracy, resilience and user gratification along with handling the issues like cold-start and data sparsity. This work expresses how scalable and flexible frameworks for tailor-made travel suggestion can be developed using hybrid intelligence system.