A hotel recommender system using context-based clustering

Abstract

The web is one of the largest textual data repositories in the world. There is voluminous data in the digital world. To search for online hotels based on specific requirements of the user is not a very easy job. Ratings and reviews available on different travel websites help to some extent but gives generalized recommendations. A recommender system (RS) which uses reviews is known as content-based and is preferred, to produce a recommendation. Proposed RS maps all requirements of a traveler to features of a hotel and produces person specific recommendation. Phrase-based Recommender System is proposed to reduce efforts and time as compared with a traditional generalized recommender system. The proposed approach makes use of hotel reviews downloaded from TripAdvisor site. The technique initiates with phrase-based feature extraction followed by iterative clustering and ends with feature mapping and exports more relevant recommendations. Betterment of a technique is proved in terms of relevance, accuracy, scalability, and consistency by comparing precision and entropy refinement and corpus size with existing technique.