ITEM BASED COLLABORATIVE FILTERING APPROACH FOR BIG DATA APPLICATION

Spurred by Service computing and cloud computing an increasing number of services are emerged in the Internet. As a result, resource -permissible data arise too big to be handled by already established techniques. Clustering along with Item based collaborative filtering has been proposed to reduce online execution time taken for processing services. Technically, these approaches sanction around two stages. In the first stage, services are divided among clusters. Each cluster has similar services. At the second stage, item based collaborative filtering algorithm is enforced on one of the cluster. Real Dataset collected from the programming web has been used to conduct several experiments.