PUREsuggest:基于引文的文献检索和可视化探索与关键词控制排名。

Fabian Beck
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引用次数: 0

摘要

通过引用可以快速识别相关研究。如果选择了多个出版物作为种子选手,就可以根据与该选题相关的传入和传出引文链接的数量,提出相关文献的具体建议。交互式地将推荐的出版物添加到选区中,可以重新生成下一个建议,并逐步建立相关的出版物集。按照这种方法,本文介绍了一种搜索和觅食方法--PUREsuggest,它将基于引文的建议与引文网络的增强可视化相结合。该方法的重点和新颖之处在于:第一,以可视化方式解释排名的透明度;第二,可以通过用户定义的关键词来引导这一过程,这些关键词反映了用户感兴趣的主题。该系统可用于建立新的文献集,更新和评估现有的文献集,以及利用收集的文献来识别该领域的相关专家。我们通过模拟会话对推荐方法进行了评估,并对界面支持的搜索策略和使用模式进行了用户研究。
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PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings.

Citations allow quickly identifying related research. If multiple publications are selected as seeds, specifc suggestions for related literature can be made based on the number of incoming and outgoing citation links to this selection. Interactively adding recommended publications to the selection refnes the next suggestion and incrementally builds a relevant collection of publications. Following this approach, the paper presents a search and foraging approach, PUREsuggest, which combines citation-based suggestions with augmented visualizations of the citation network. The focus and novelty of the approach is, frst, the transparency of how the rankings are explained visually and, second, that the process can be steered through user-defned keywords, which refect topics of interests. The system can be used to build new literature collections, to update and assess existing ones, as well as to use the collected literature for identifying relevant experts in the feld. We evaluated the recommendation approach through simulated sessions and performed a user study investigating search strategies and usage patterns supported by the interface.

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