Hotel recommendation system using machine learning - Wong, Wai On (2025)
In recent times, choosing the appropriate hotel destination and making bookings has become increasingly complex due to the rapidly growing volume of available online information. The importance of recommender systems (RSs) is rising as they help users make informed decisions and provide comprehensive insights into products or services. Managing user-generated data such as votes, ratings, views, and reviews presents significant challenges. There are three objectives in the study, which is to perform data preprocessing on the Google Reviews dataset for hotels in Perak using an instant data scraper, to develop three suitable machine learning models on the cleaned dataset and evaluate their performance, and to propose a recommendation system based on the developed machine learning models. The methodology includes data scraping, preprocessing, implementation of machine learning techniques such as Naïve Bayes, Random Forest, and Support Vector Machine (SVM), and proposes a recommendation system. The system integrates these models to provide hotel recommendations based on each user's preferences. The results show that the proposed model is effective and generates recommendations for the user. The future work includes expanding the dataset, refining the recommendation algorithm, using natural language processing techniques with the addition of multilingual reviews, and deploying the system as a user-friendly application or mobile application.