【摘要】 【背景】科技论文数量的快速增长使得如何快速查找或定位到感兴趣的文献资料成为了科研人员在科学研究过程中一个亟待解决的问题。【目的】本文旨在研究并提出一种基于图嵌入的论文推荐算法，尝试解决面向用户的论文个性化推荐问题。【方法】本文提出了一种基于异构图嵌入的论文个性化推荐算法。该算法通过异构图嵌入模型构建论文节点的嵌入表示，同时基于作者已发表的论文构建该作者的兴趣表示，最终利用两者之间的相似度对作者进行论文推荐。【结论】在DBLP数据集上的实验证明了本文提出的模型及算法的有效性。更多还原

【Abstract】 [Background] With the rapid growth of the number of scientific papers, finding or locating the papers of interest has become an urgent problem for researchers in the process of scientific research. [Objective] This paper aims to study a paper recommendation algorithm to solve the problem of user-oriented personalized paper recommendations. [Methods] A personalized paper recommendation algorithm based on heterogeneous graph embedding is proposed, which learns the representation of the paper nodes, then the interest of the author is calculated according to the papers published by the author, and recommendations are then generated based on the similarity between author interests and the papers. [Results] Experiment on the DBLP dataset demonstrates the effectiveness of the model and the algorithm proposed in this paper.更多还原