A unified graph model for personalized query-oriented reference paper recommendation
A unified graph model for personalized query-oriented reference paper recommendation
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DOI：
10.1145/2505515.2507831
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发表时间：
2013-10
期刊：
影响因子：
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通讯作者：
Fanqi Meng;D. Gao;Wenjie Li;Xu Sun;Yuexian Hou
中科院分区：
文献类型：
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作者：
Fanqi Meng;D. Gao;Wenjie Li;Xu Sun;Yuexian Hou
随着研究出版物的数量越来越多，为研究人员提供关于研究领域或主题的参考文献列表的快速而准确的推荐变得越来越重要。在本文中，我们提出了一个统一的图模型，它可以很容易地整合各种类型的有用信息(如内容、作者、引文和合作网络等)。以获得高效的推荐。该模型不仅可以深入探索如何更好地组合这些类型的信息，而且还使面向查询的个性化参考文献推荐成为可能，这是一个过去没有明确解决的新问题。实验表明，与非个性化推荐相比，个性化推荐具有明显的优势。
With the tremendous amount of research publications, it has become increasingly important to provide a researcher with a rapid and accurate recommendation of a list of reference papers about a research field or topic. In this paper, we propose a unified graph model that can easily incorporate various types of useful information (e.g., content, authorship, citation and collaboration networks etc.) for efficient recommendation. The proposed model not only allows to thoroughly explore how these types of information can be better combined, but also makes personalized query-oriented reference paper recommendation possible, which as far as we know is a new issue that has not been explicitly addressed in the past. The experiments have demonstrated the clear advantages of personalized recommendation over non-personalized recommendation.