基于作者偏好的学术投稿刊物推荐算法

Academic journal contribution recommendation algorithm based on author preferences

针对投稿刊物推荐算法总是单独考虑文本主题或者作者历史发刊记录,导致投稿刊物推荐结果准确率低的问题,提出了一种基于作者偏好的学术刊物投稿推荐算法。该算法不仅协调使用了文本主题和作者历史发刊记录,还挖掘了投稿刊物的学术焦点与时间的潜在联系。首先,使用潜在狄利克雷(LDA)主题模型对文章标题进行主题提取;其次,建立主题-刊物和时间-刊物的模型图,并采用大规模信息网络嵌入(LINE)模型学习异构图节点的嵌入;最后,融合作者的主题偏好和历史发刊记录来计算刊物的综合得分,并据此对投稿作者进行投稿刊物推荐。在两个公开数据集DBLP和PubMed上的实验结果表明,相比奇异值分解(SVD)、DeepWalk、非负矩阵分解(NMF)等6个算法,所提出的算法在不同推荐的投稿刊物列表长度的情况下的召回率均为最优,并且在需要从论文和知识库中获取更少信息的同时,保持了较高的准确性,能有效提高投稿刊物推荐算法的鲁棒性。

In order to solve the problem that the algorithms of publication venue recommendation always consider the text topics or the author’s history of publications separately,which leads to the low accuracy of publication venue recommendation results,a contribution recommendation algorithm of academic journal based on author preferences was proposed.In this algorithm,not only the text topics and the author’s history of publications were used together,but also the potential relationship between the academic focuses of publication venues and time were explored.Firstly,the Latent Dirichlet Allocation(LDA)topic model was used to extract the topic information of the paper title.Then,the topic-journal and time-journal model diagrams were established,and the Large-scale Information Network Embedding(LINE)model was used to learn the embedding of graph nodes.Finally,the author’s subject preferences and history of publication records were fused to calculate the journal composite scores,and the publication venue recommendation for author to contribute was realized.Experimental results on two public datasets,DBLP and PubMed,show that the proposed algorithm has better recall under different list lengths of recommended publication venues compared to six algorithms such as Singular Value Decomposition(SVD),DeepWalk and Non-negative Matrix Factorization(NMF).The proposed algorithm maintains high accuracy while requiring less information from papers and knowledge bases,and can effectively improve the robustness of publication venue recommendation algorithm.