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A Parallel Personalized Recommendation Algorithm using Bipartite Graphs
Hao Huang,Sotirios G. Ziavras,Yaojie Lu 보안공학연구지원센터 2016 International Journal of u- and e- Service, Scienc Vol.9 No.7
BDM-NBI algorithm is proposed in this paper. It focuses on the analysis of a personalized recommendation algorithm that utilizes a weighted bipartite graph suitable for processing big data. To improve the performance of this recommendation algorithm through parallel processing techniques, a sparse matrix partitioning algorithm is then developed that uses the bipartite graph as input. Our algorithm adopts bipartite graph partitioning using a vertex separator method that partitions a high-dimensional sparse matrix into a pseudo-block based diagonal matrix. Then, the recommendation algorithm analyzes all weighted sub-matrices in parallel. We produce the global recommendation weighted matrix by merging all of the sub-matrices in parallel. Experiments with Hadoop show that our algorithm has good approximation for small matrices and excellent scalability.