科研兴趣空间挖掘与多任务推荐研究

doi:10.3772/j.issn.1000-0135.2023.10.008

情报学报

2023, Vol. 42

Issue (10): 1224-1237 DOI: 10.3772/j.issn.1000-0135.2023.10.008

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科研兴趣空间挖掘与多任务推荐研究

崔鸿飞¹, 冯子函², 张靖雨³

1.北京科技大学经济管理学院,北京 100083
2.北京航空航天大学经济管理学院,北京 100191
3.爱丁堡大学商学院,爱丁堡 EH8 9JU

Research Interest Space Mining and Double-Task Research Recommendation Based on Heterogeneous Network Embedding

Cui Hongfei¹, Feng Zihan², Zhang Jingyu³

1.School of Economics and Management, University of Science and Technology Beijing, Beijing 100083
2.School of Economics and Management, Beihang University, Beijing 100191
3.University of Edinburgh Business School, the University of Edinburgh, Edinburgh EH8 9JU

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摘要丰富的互联网文献数据库是科研人员了解领域发展和前沿的重要资源，从全局视角对领域的海量科研成果进行高效信息挖掘，可以在知识洪流中为科研人员提供更加明确的方向。本研究基于经典生物医学文献数据库PubMed收录的发表于2010—2021年的13万篇文章，挖掘科研人员的历史行为信息，构建同时包含作者、论文、关键词的异质信息网络，利用异质信息网络表示学习算法metapath2vec将该网络嵌入成为异质向量空间，并通过计算异质向量空间中向量的相似度指标，同时实现科研合作者推荐与科研兴趣关键词推荐。与已有研究相比，本研究的方法更加重视多任务协同，不仅在新增的科研兴趣关键词的任务中获得了有意义的推荐结果，还显著提高了科研合作者推荐的准确度。同时，本研究在作者空间与关键词空间进行了深入挖掘，并证明其在科研兴趣的语义理解方面具有指导意义。本研究在科研兴趣的研究、挖掘与推荐方面提供了新的研究视角。

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	崔鸿飞
	冯子函
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关键词 ：异质信息网络, 网络表示学习, 合作者推荐, 关键词推荐, 科研兴趣空间

收稿日期: 2023-01-02

作者简介: 崔鸿飞，女，1988年生，博士，讲师，硕士生导师，主要研究领域为大数据信息挖掘、机器学习方法，E-mail：cuihf06@hotmail.com；冯子函，男，2000年生，博士研究生，主要研究领域为深度学习、数据挖掘；张靖雨，男，2000年生，硕士研究生，主要研究领域为商业分析、数据挖掘；

引用本文:

崔鸿飞, 冯子函, 张靖雨. 科研兴趣空间挖掘与多任务推荐研究[J]. 情报学报, 2023, 42(10): 1224-1237.
Cui Hongfei, Feng Zihan, Zhang Jingyu. Research Interest Space Mining and Double-Task Research Recommendation Based on Heterogeneous Network Embedding. 情报学报, 2023, 42(10): 1224-1237.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2023.10.008 或 https://qbxb.istic.ac.cn/CN/Y2023/V42/I10/1224

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