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A Keyword Sensitive Service Recommendation on Map Reduce for Hotel Information

G. Dhinesh kumar, R.S. Ponmagal, P. Dineshkumar


Administration recommender frameworks have been demonstrated as important apparatuses for giving fitting suggestions to clients. In the most recent decade, the measure of clients, administrations and online data has developed quickly, yielding the huge information examination issue for administration recommender frameworks. Subsequently, conventional administration recommender frameworks frequently experience the ill effects of adaptability and wastefulness issues when transforming or dissecting such expansive scale information. In addition, the majority of existing administration recommender frameworks show the same evaluations and rankings of administrations to diverse clients without considering various clients' inclination, and thusly neglects to meet clients' customized prerequisites. In this paper, we propose a Keyword-Aware Service Recommendation system, named KASR, to address the above difficulties. It goes for showing a customized administration suggestion rundown and prescribing the most fitting administrations to the clients adequately. In particular, catchphrases are utilized to show clients' inclination, and a client based Collaborative Filtering calculation is received to create proper suggestions. To enhance its versatility and effectiveness in enormous information environment, KASR is actualized on Hadoop, a broadly received dispersed processing stage utilizing the MapReduce parallel handling ideal model. At long last, broad tests are directed on genuine information sets, and results exhibit that KASR fundamentally enhances the exactness and adaptability of administration recommender frameworks over existing methodologies.


Recommender System, Preference, Keyword, Big Data, MapReduce, Hadoop

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