融合作者合作强度与研究兴趣的合作者推荐

Collaborator recommendation via integrating author’s cooperation strength and research interest

合作者推荐是科研社交网络中的一个重要应用,为科研人员推荐适合的合作者有利于增强学术合作、提升作者的合作交流。为此,提出CRISI方法,从而有效地推荐与待推荐作者合作强度高且研究兴趣比较相似的同行,并且还考虑了作者间的合作强度(结构)、研究兴趣(属性)相似度和待推荐作者形成的社区紧密程度等信息。具体地,首先,基于作者与文献的关系构建作者合作关系属性图;然后,计算作者合作强度与研究兴趣相似度并据此构建双加权网络;再次,探测影响力高且合作强度大的作者节点作为种子;最后,设计分数k-core社区搜索方法找到与待推荐作者合作关系紧密的社区。实验结果表明,CRISI方法相比现有方法获得了显着的性能提升。

Collaborator recommendation is an important application in research social networks.Recommending suitable collaborators for researchers is conducive to enhance academic cooperation and improve the collaborative exchanges among authors.To this end,CRISI method is proposed to effectively recommend peers with high intensity of cooperation and similar research interests.Our method considers the cooperation intensity(structure)between authors,the similarity of research interest(attribute),as well as the closeness of the community formed by the authors to be recommended.Specifically,firstly,an attribute graph of the author’s cooperative relationship is constructed based on the relationship between the author and the literature.Then,the author’s cooperation intensity and research interest similarity are calculated and the dual-weighted network is constructed accordingly.Thirdly,the author nodes with high influence and strong cooperation intensity are detected as seed.Finally,a fractional k-core community search method is designed to find a community that has a close working relationship with the author to be recommended.The experimental results show that CRISI method can achieve significant performance improvement over the existing methods.