Sentiment-enhanced Neural Collaborative Filtering Models Using Explicit User Preferences

The integration of recommender systems contributes to the tourism industry as it provides tailored recommendations to users, assisting them in discovering and selecting the most suitable accommodation options based on their particular needs and preferences. By providing personalized recommendations that are tailored to each user's preferences and needs, hotel rec-ommendation systems could assist in reducing the time and effort required to find the best hotel options. In addition, users could discover new and relevant accommodation alternatives that they might not have previously considered. Despite the importance of the reasons underlying user preferences, existing review-based recommendation systems often neglect the importance of sentiment words linked to related item aspects. To address this need, this study presents a sentiment-enhanced hotel recommender system using neural collaborative filtering that incorporates information derived from both textual reviews and user-hotel relationships. This study employs a neural collaborative filtering approach to learn the relationship between user-hotel interactions and a sentiment-enhanced recommendation system. In regards to the experiment conducted in this study, our method enhances the model's ability to capture user preferences and item features through information from sentiment-enhanced text reviews in comparison to sub-ratings generated by users. Aspect-based sentiment analysis improves personalized hotel recommendations by taking into account the sentiment toward specific aspects of the hotel, such as cleanliness, service, or location.