Recently, the application of Artificial Intelligence algorithms in hotel recommendation systems has become an increasingly popular topic. One such method that has proven to be effective in this field is Deep Learning, especially Natural Language processing models, which are able to extract semantic knowledge from user's text reviews to create more efficient recommendation systems. This can lead to the development of intelligent models that can classify a user's preferences and emotions based on their feedback in the form of text reviews about their hotel stay experience. In this study, we propose a Natural Language Processing framework that utilizes customer text reviews to provide personalized recommendations for the most appropriate hotel based on their preferences. The framework is based on Bidirectional Encoder Representations from Transformers (BERT) and a fine-tuning/validation pipeline that categorizes customer hotel review texts into "Bad," "Good," or "Excellent" recommended hotels. Our findings indicate that the hotel recommendation system we propose can significantly enhance the user experience of booking accommodations by providing personalized recommendations based on user preferences and previous booking history.

One of the first things to do while planning a trip is to book a good place to stay. Booking a hotel online can be an overwhelming task with thousands of hotels to choose from, for every destination. Motivated by the importance of these situations, we decided to work on the task of recommending hotels to users. W e used Expedia's hotel recommendation dataset, which has a variety of features that helped us achieve a deep understanding of the process that makes a user choose certain hotels over others. The aim of this hotel recommendation task is to predict and recommend five hotel clusters to a user that he/she is more likely to book given hundred distinct clusters.