A Clustering Based Hybrid Recommendation System for Services in Big Data

Big data deals with large volume of complex growing data set with multiple autonomous sources. With the growing technologies, data storage and data collection capacity goes increases day-by-day, big data are now rapidly expanding in all fields. It tends to increase services on internet. So, the service relevant data become too vast to process by traditional approaches. It becomes difficult for users to select best product from so many products which are available. These systems suffer from scalability, data sparsity, and cold-start problems resulting in poor quality recommendations. In order to view this problem this paper provides a hybrid recommendation system which will satisfy the users according to their needs and interest and increase the overall performance of the system. The main idea is using hybrid recommendation techniques to suppress the drawbacks of the traditional techniques or an individual technique in a combined model. Paper presents a system to improve the accuracy of recommendation in big data application.