基于分布式图计算的学术论文推荐算法

Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-04-12 Cooperative journals: 《计算机应用研究》

潘峰 怀丽波 崔荣一

Abstract： Aiming at the low efficiency caused by massive academic paper data, this paper proposed a recommendation algorithm method based on the hierarchical mixed model named WSVD++. According to the structural features of academic papers, the model constructs a weighted bipartite graph model. Firstly, this method extracted the features of each paper and constructs the composite relation graph according to the ratio of different features. Secondly, it uses an improved PPR algorithm on the graph to calculate the importance weight of each paper, and then weighs the relation between the user and the paper. Finally, it recommend on the weighted bipartite graph by using SVD++ graph algorithm. The result shows that the proposed algorithm improves the recommended accuracy. The whole process implemented in distributed graph calculation system, that means the method has good expansibility and is suitable for big data processing.

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