An Efficient Personalized Hotel Recommendation System for Big Data Applications

Abstract: Administration recommender frameworks have been indicated as important apparatuses for giving proper proposals to clients. In the most recent decade, the measure of clients, administrations and online data has developed quickly, yielding the enormous information examination issue for administration recommender frameworks. Also the greater part of existing administration recommender frameworks, exhibit the same appraisals and rankings of administrations to distinctive clients without considering various clients inclination, and hence neglects to meet clients customized necessities. Consequently we approach a customized administration proposal rundown for the most suitable administrations to clients, by proposing a keyword-aware suggestion strategy and Natural Language Processing, to address the above difficulties. Particularly, keywords are utilized to demonstrate clients inclination, and a client based Collaborative Filtering calculation is received to create proper suggestions. To enhance its versatility and productivity in vast information environment, it is actualized on Hadoop platform, a generally embraced appropriated figuring stage utilizing the Map Reduce parallel transforming framework. At long last, far reaching trials are directed on certifiable information datasets, and results exhibit that personalized search technique fundamentally enhances the exactness and versatility of administration recommender frameworks over existing methodologies.

Keywords:

Recommender System , Preference , Keyword , Big Data , Map Reduce , Hadoop , Hotels