Leveraging publication metadata and social data into FolkRank for scientific publication recommendation

Stephan Doerfel, R. Jäschke, A. Hotho, Gerd Stumme
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引用次数: 20

Abstract

The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.
利用出版物元数据和社会数据到FolkRank进行科学出版物推荐
不断增长的新科学论文需要新的检索机制。缓解这种信息超载现象的一种方法是协作标记系统,它允许用户选择、共享和注释对出版物的引用。这些系统采用推荐算法向用户提供个性化的有趣和相关出版物列表。在本文中,我们分析了将来自协作标记系统的社会数据和元数据整合到基于图的排名算法FolkRank中的不同方法,并利用它向社会书签系统BibSonomy的用户推荐科学文章。我们将结果与协作过滤的结果进行比较,协作过滤先前已应用于资源推荐。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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