Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 50-56.DOI: 10.11772/j.issn.1001-9081.2021010185

• Artificial intelligence • Previous Articles     Next Articles

Academic journal contribution recommendation algorithm based on author preferences

Yongfeng DONG1,2,3, Xiangqian QU1,2,3, Linhao LI1,2,3(), Yao DONG1,2,3   

  1. 1.School of Artificial Intelligence,Hebei University of Technology,Tianjin 300401,China
    2.Hebei Province Key Laboratory of Big Data Calculation (Hebei University of Technology),Tianjin 300401,China
    3.Hebei Data Driven Industrial Intelligent Engineering Research Center (Hebei University of Technology),Tianjin 300401,China
  • Received:2021-02-03 Revised:2021-03-27 Accepted:2021-04-14 Online:2021-04-29 Published:2022-01-10
  • Contact: Linhao LI
  • About author:DONG Yongfeng, born in 1977, Ph. D., professor. His research interests include artificial intelligence, knowledge graph.
    QU Xiangqian, born in 1995, M. S. candidate. His research interests include recommended system, big data and intelligent computing.
    LI Linhao, born in 1989, Ph. D., lecturer. His research interests include intelligent monitoring, knowledge tracking.
    DONG Yao, born in 1982, Ph. D. candidate, senior experimentalist. Her research interests include knowledge graph, data mining.
  • Supported by:
    National Natural Science Foundation of China(61902106);Natural Science Foundation of Tianjin(19JCZDJC40000);Beidou Technology Transformation and Industrialization Foundation of Beihang(BARI2001);Science and Technology Research Project of Hebei Province Colleges and Universities(QN2021213)


董永峰1,2,3, 屈向前1,2,3, 李林昊1,2,3(), 董瑶1,2,3   

  1. 1.河北工业大学 人工智能与数据科学学院, 天津 300401
    2.河北省大数据计算重点实验室(河北工业大学), 天津 300401
    3.河北省数据驱动工业智能工程研究中心(河北工业大学), 天津 300401
  • 通讯作者: 李林昊
  • 作者简介:董永峰(1977—),男,河北定州人,教授,博士,CCF会员,主要研究方向:人工智能、知识图谱
    李林昊(1989—), 男, 山东威海人,讲师,博士,CCF会员,主要研究方向: 智能监控、知识追踪
  • 基金资助:


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.

Key words: academic journal, bipartite graph, publication venue recommendation, graph embedding, author preference



关键词: 学术刊物, 二部图, 投稿推荐, 图嵌入, 作者偏好

CLC Number: