User Preferences Based Recommendation System for Services using Mapreduce Approach

Service recommendations based on the user preferences using keyword aware service recommendation system simply called as KASR. Here the keyword shows the preference of the user. Based on the keyword service, recommendations are provided for the user. For this process we use a user-based collaborative filtering algorithm. To improve the efficiency of this process we implement KASR in Hadoop environment which is a open-source software framework for storing data and running applications on clusters of commodity hardware. It provides massive storage for any kind of data, enormous processing power and the ability to handle virtually limitless concurrent tasks or jobs. To improve the efficiency and scalability of the KASR we proposed the combined preferences using rank boosting algorithm. In the rank boosting algorithm, it gets the input as combined preferences, based on the preferences it process the similarities with the reviews of the existing users then it provides the ranking to the services. Based on the ranking provided to the services we generate the output recommendations with high similarity matching results as the recommendation list to the end users for their combined preferences.