Valence arousal similarity based recommendation services

Abstract

Web Services play a vital role in e-commerce and e-business applications. A WS (Web Service) application is interoperable and can work on any platform i.e.; platform independent, large scale distributed systems can be established easily. A Recommender System is a precious tool for providing appropriate recommendations to all users in a Hotel Reservation Website. User based, Top k and profile based approaches are used in collaborative filtering algorithm which does not provide personalized results to the users and inefficiency and scalability problem also occurs due to the increase in the size of large datasets. To address the above mentioned challenges, a Valence-Arousal Similarity based Recommendation Services, called VAS based RS, is proposed. Our proposed mechanism aims to presents a personalized service recommendation list and recommending the most suitable service to the end users. Moreover, it classifies the positive and negative preferences of the users from their reviews to improve the prediction accuracy. For improve its efficiency and scalability in big data environment, VAS based RS is implemented using collaborative filtering algorithm on MapReduce parallel processing paradigm in Hadoop, a widely-adopted distributed computing platform.