【摘 要】
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In this paper, BDM-NBI algorithm under Spark framework is proposed.It focuses on the analysis of a personalized recommendation algorithm that utilizes a weighted bipartite graph suitable for processin
【机 构】
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School of Information Technology,University of International Business and Economics
论文部分内容阅读
In this paper, BDM-NBI algorithm under Spark framework is proposed.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 Spark show that our algorithm has good approximation for small matrices and excellent scalability.
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