A Natural Language Processing Framework for Hotel Recommendation Based on Users' Text Reviews
This work addresses personalized hotel booking for users by improving recommendation accuracy, but it is incremental as it applies existing NLP methods to a specific domain.
The authors tackled hotel recommendation by developing a Natural Language Processing framework using BERT to analyze user text reviews, categorizing hotels as 'Bad,' 'Good,' or 'Excellent' to provide personalized recommendations, though no concrete performance numbers are provided.
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.