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Learning to Rank for Expert Search in Digital Libraries of Academic Publications

机译:学习在学术出版物的数字图书馆中排名为专家搜索

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The task of expert finding has been getting increasing attention in information retrieval literature. However, the current state-of-the-art is still lacking in principled approaches for combining different sources of evidence in an optimal way. This paper explores the usage of learning to rank methods as a principled approach for combining multiple estimators of expertise, derived from the textual contents, from the graph-structure with the citation patterns for the community of experts, and from profile information about the experts. Experiments made over a dataset of academic publications, for the area of Computer Science, attest for the adequacy of the proposed approaches.
机译:专家发现的任务在信息检索文献中一直在越来越关注。然而,目前的最先进仍然缺乏以最佳方式结合不同的证据来源的方法。本文探讨了学习的用法,以与专家社区社区的引文结构,以及专家的简介信息,从图形结构中探索了与文本内容的多种专业知识估算器的主要方法。在学术出版物的数据集中进行的实验,为计算机科学领域,证明了提出方法的充分性。

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