An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

Abstract

In recent times, selection of a suitable hotel location and reservation of accommodation have become a critical issue for the travelers. The online hotel search has been increased at a very fast pace and became very time-consuming due to the presence of huge amount of online information. Recommender systems (RSs) are getting importance due to their significance in making decisions and providing detailed information about the required product or a service. To acquire the hotel recommendations while dealing with textual hotel reviews, numerical ranks, votes, ratings, and number of video views have become difficult. To generate true recommendations, we have proposed an intelligent approach which also deals with large-sized heterogeneous data to fulfill the needs of the potential 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 for personalized recommendation. The developed system not only has the ability to handle heterogeneous data using big data Hadoop platform but it also recommends hotel class based on guest type using fuzzy rules. Different experiments are performed over the real-world datasets obtained from two hotel websites. Moreover, the values of precision and recall and F-measure have been calculated, and the results are discussed in terms of improved accuracy and response time, significantly better than the traditional approaches.