Research Article AnIntelligentDataAnalysisforRecommendationSystemsUsing Machine Learning Bushra Ramzan, 1 ImranSarwarBajwa , 1 Noreen Jamil, 2 RiazUlAmin, 3 Shabana Ramzan, 4 Farhan Mirza, 5 andNadeemSarwar 6 1 Department of Computer Science & IT, e Islamia University, Bahawalpur 63100, Pakistan 2 Department of Computer Science, FAST National University, Islamabad, Pakistan 3 Faculty of Computing, BUITEMS, 83100 Quetta, Pakistan 4 Department of Computer Science, Govt. Sadiq College Women University, Bahawalpur, Pakistan 5 School of Engineering, Computer & Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand 6 Department of Computer Science, Bahria University, Lahore, Pakistan Correspondence should be addressed to Imran Sarwar Bajwa; imran.sarwar@iub.edu.pk Received 27 May 2019; Revised 13 August 2019; Accepted 30 August 2019; Published 31 October 2019 Guest Editor: Aibo Song Copyright © 2019 Bushra Ramzan et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In recent times, selection of a suitable hotel location and reservation of accommodation have become a critical issue for the travelers. e 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. e 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). e proposed system recommends hotels based on the hotel features and guest type for personalized recommendation. e 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. 1.Introduction In the modern era of advancing web technologies, the recommender systems (RSs) have turned the notice of the business society and the common man towards itself due to its significance and importance in the e-commerce and achievement of superior customer’s approval. Nowadays e-commerce is believed to be strongly connected to the customer’s satisfaction, and an ultimate success is always dependent on customer loyalty. e same is with the online booking and reservation systems being a main component of the tourism industry. Mariani et al. [1] discussed that the most powerful and popular industry which has a major impact on total GDP of world economy is tourism. Tourists around the world are always looking for best hotels for their residence during the tours which keeps the recommender systems as their primary choice to obtain best available hotel choices for online reservations well before reaching their Hindawi Scientific Programming Volume 2019, Article ID 5941096, 20 pages https://doi.org/10.1155/2019/5941096