  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.
