Measuring Researcher Relatedness with Changes in Their Research Interests
Measuring Researcher Relatedness with Changes in Their Research Interests
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DOI：
10.23919/apsipa.2018.8659506
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Hiroyuki Nishizawa;Marie Katsurai;I. Ohmukai;Hideaki Takeda
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文献类型：
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作者：
Hiroyuki Nishizawa;Marie Katsurai;I. Ohmukai;Hideaki Takeda
相关的研究者推荐对于寻找潜在的研究合作者很重要，现有的几种方法是根据研究者的研究兴趣来衡量研究者的相关性。我们之前的作品用一个多维主题向量来表示研究人员，这个多维主题向量是从研究人员的出版物中计算出来的，忽略了出版日期。另一方面，最近关于信息推荐的研究表明，随着时间的推移，对用户偏好变化的建模是有效的。因此，本文提出了一种新的研究人员表示方法，该方法由年度主题向量组成。为了衡量研究人员之间的相关性，我们使用动态时间扭曲计算两个主题向量序列之间的相似度。实验结果表明，与传统方法相比，该方法可以有效地发现目标研究人员的主题转移随时间的变化相似的研究人员。
Relevant researcher recommendation is important for finding potential research collaborators, and several existing methods measure researcher relatedness based on their research interests. Our previous works represented a researcher with a single multidimensional topic vector calculated from the researcher's publications, ignoring the publication dates. On the other hand, recent studies on information recommendation have shown the effectiveness of modeling changes in user preferences over time. Thus, this paper proposes a new representation of researchers, which consists of yearly topic vectors. To measure the relatedness between researchers, we calculate the similarity between two sequences of topic vectors using Dynamic Time Warping. An experimental example visualizes topic transitions of a target researcher and demonstrates that the proposed method can effectively find researchers whose topic transitions are similar over time, when compared to the conventional method.