An Intelligent Data Analysis for Hotel Recommendation Systems using Machine Learning
This work addresses the need for more accurate and efficient hotel recommendation systems for customers, though it is incremental as it builds on existing collaborative filtering methods.
The paper tackles the problem of generating personalized hotel recommendations by handling heterogeneous and large-sized data, achieving improved accuracy and response time compared to traditional approaches, with results discussed using precision, recall, and F-measure metrics.
This paper presents an intelligent approach to handle heterogeneous and large-sized data using machine learning to generate true recommendations for the future customers. The Collaborative Filtering (CF) approach is one of the most popular techniques of the RS to generate recommendations. We have proposed a novel CF recommendation approach in which opinion based sentiment analysis is used to achieve hotel feature matrix by polarity identification. Our approach combines lexical analysis, syntax analysis and semantic analysis to understand sentiment towards hotel features and the profiling of guest type (solo, family, couple etc). The proposed system recommends hotels based on the hotel features and guest type as additional information for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommend hotel class based on guest type using fuzzy rules. Different experiments are performed over the real world dataset obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated and results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.