Hotel Recommendation Using Machine Learning
Keywords:
Machine Learning, natural language processing (NLP), user-centric recommendation systemsAbstract
This project introduces a Hotel Recommendation System built using Machine Learning techniques and a flexible plugin-based architecture to deliver highly personalized and context-aware hotel suggestions for users. The system adopts a hybrid recommendation approach, combining contentbased filtering, collaborative filtering, and natural language processing (NLP) for sentiment analysis on user reviews. This multi-faceted strategy enables the system to better understand user preferences, past behavior, and the sentiment behind user-generated content to generate accurate and meaningful hotel recommendations.
A key feature of this system is its plugin integration framework, which allows seamless incorporation of external services such as location-based APIs, real-time weather updates, travel platforms (e.g., Expedia, Booking.com), and user authentication
modules. These plugins enhance the system’s capabilities by offering contextually relevant suggestions—such as proximity to landmarks, local weather conditions, seasonal trends, and price optimization—improving the overall user experience. The machine learning models are trained on comprehensive hotel datasets and finetuned using performance metrics such as precision, recall, F1-score, and RMSE. The architecture ensures modularity, scalability, and adaptability for future enhancements or domain transfers. This project demonstrates the potential of combining machine learning with modular plugin design to create intelligent, dynamic, and
user-centric recommendation systems, particularly in the travel and hospitality domain.
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References
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