基于分布式图计算的学术论文推荐算法

针对海量论文数据导致的应用效率低下问题，提出一个基于层次混合模型的推荐算法WSVD++。该模型根据学术论文良好的结构特征，构建一个加权的论文二部图模型。首先对论文进行特征提取，按不同特征的权重构建论文的复合关系图；其次对关系图采用一种改进的PPR算法，计算每篇论文的重要程度，依此来对用户—论文关系进行加权；然后在构建好的加权二部图模型上混合SVD++图算法进行推荐。实验结果表明，改善了推荐算法学术论文的推荐效果，并且基于分布式图计算框架GraphX，扩展性好，适合大数据处理。

Academic paper recommendation based on distributed graph

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.