An Efficient Recommender System based on Collaborative Filtering

Abstract

In general Big Data enterprise large-volume of complex, growing data sets with multiple, autonomous sources. The utmost underlying challenge for the Big Data applications is to explore the large volumes of data and extract useful information or knowledge for future actions. In view of this challenge, we propose a method called Clustering based Collaborative Filtering approach. It consists of two stages: clustering and Collaborative Filtering. Clustering is an initial step to separate big data into manageable parts. A cluster contains some similar services. In the second stage, a Collaborative Filtering algorithm is applied on one of the clusters. As the number of services in a cluster is much less than the total number of services, the computation time of collaborative filtering algorithm can be reduced significantly. Besides, since the ratings of similar services within a cluster are more relevant than that of dissimilar services, the recommendation accuracy based on user ratings may be enhanced.