The Recommended Online Social Networks (OSN) and Big Data System Using Large Scale Graph Partitioning with Mahout and PowerGraph

Abstract

Introduction : Social Big data is generated by interactions of connected users on social network. Sharing of opinions and contents amongst users, reviews of users for products, result in Social Big data. If any user intends to select product such as movies, books etc. from e-commerce sites or view any topic or opinion on social networking sites, there are a lot of options and these options result in information overload. Case Description : Social recommendation systems assist users to make better selection as per their likings. Recent research works have improved recommendation systems by using matrix factorization, social regularization or social trust inference. Furthermore, these improved systems are able to alleviate cold start and sparsity but not efficient for scalability. Discussion and Evaluation: The main focus of this paper is to improve scalability and provide better recommendations to users with large-scale data in less response time. We have partitioned social big graph and distributed it on different nodes based on Mahout and PowerGraph like system. Conclusion : In our approach, partitioning is based on direct as well as indirect trust between users and comparison with state-of-the-art approaches proves that statistically better partitioning quality is achieved using our approach. In our proposed approach ScaleRec, hyperedge and transitive closure are used to enhance social trust amongst users. Experiment analysis on standard datasets proves that better locality and recommendation accuracy is achieved by using our proposed approach.