Description

In the existing approach they propose a keyword aware service recommendation system, simply called as KASR. In the system ,it provides service recommendations based on the user preferences. Here the keyword shows the preference of the user.Based on the keyword service recommendations are provided for the user. For this process here we use a user-based collaborative filtering algorithm. To improve the efficiency of this process we implement this in Hadoop environment. It doesn’t give the good solution for the scalability and inefficiency problem when processing or analyzing such large-scale data. To improve the efficiency and scalability of the process 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. Finally it generates high similarity matching results as the recommendation list to the end users for their combined preferences.