基于内容的学术专家发现推荐

César Albusac, L. M. D. Campos, J. M. Fernández-Luna, J. Huete
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引用次数: 0

摘要

如今,网络用户越来越频繁地搜索专业人员,以便找到能够帮助解决特定领域任何问题的人。这就是所谓的专家发现。一个特殊的例子是当用户对科研人员感兴趣时。相关的问题是,给定一个表达用户感兴趣的主题的查询,获得一组精通该主题的研究人员。解决这个问题的困难之一是确定专业人士擅长的主题。在本文中,我们从基于内容的推荐的角度来面对这一问题,我们提出了一种方法,从每个研究者发表的文章开始,通过查询得到专家研究者。我们还提出了一个新的文档集合,称为PMSC-UGR,专门为专家寻找和文档过滤领域的评估而设计
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Content-based recommendation for Academic Expert finding
Nowadays it is more and more frequent that Web users search for professionals in order to find people who can help solve any problem in a given field. This is call expert finding. A particular case is when users are interested in scientific researchers. The associated problem is to get, given a query that expresses a topic of interest for a user, a set of researchers who are expert on it. One of the difficulties to tackle the problem is to indentify the topics in which a professional is expert. In this paper, we face this problem from a content-based recommendatation perspective and we present a method where, starting from the articles published by each researcher, and a query, the expert researchers are obtained. We also present a new document collection, called PMSC-UGR, specifically designed for the evaluation in the field of expert finding and document filtering
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