Integrating Machine Learning Algorithms For Personalized Motel Recommendations
DOI:
https://doi.org/10.52783/jns.v14.2660Keywords:
Hotel Rating, Data Balancing, Recursive Feature Elimination (RFE), Review Analysis, Customer Satisfaction, Machine Learning Algorithm (SVM, Naïve Bayes, KNN)Abstract
Earlier we have seen a eloquent rise in the use of recommendation systems in many different businesses, including the entertainment and tourist sectors. This abstract explores the integration of machine learning algorithms to enhance personalized hotel recommendations. They can perform the function of information filters by processing essential data from a variety of networks to provide consumer ideas that are relevant to their needs. When choosing hotels in cities all over the globe, many visitors and travelers often depend on written reviews, numerical ratings, and specific areas of interest. User preferences have a big impact on hotel recommendations. The most effective recommendations may be made by recommendation systems by utilizing historical user preference data.To solve this problem, recommender systems have suggested content-based filtering methods.
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