PUREsuggest：基于引文的文献搜索与关键词控制排序的可视化探索
PUREsuggest: Citation-based Literature Search and Visual Exploration with Keyword-controlled Rankings
AI总结：
本文提出 PUREsuggest，利用引文链接、可视化排序解释和用户关键词控制，支持文献搜索、集合构建评估及专家发现，并通过模拟会话和用户研究验证。
摘要：
引文能够快速识别相关研究。如果选择多篇出版物作为种子，就可以根据与该选择之间的入站和出站引文链接数量，给出相关文献的具体建议。以交互方式将推荐出版物加入选择，可优化后续建议，并逐步构建一个相关的出版物集合。遵循这一方法，本文提出了一种搜索与搜寻方法 PUREsuggest，将基于引文的建议与增强的引文网络可视化相结合。该方法的重点和新颖之处首先在于排序如何获得可视化解释的透明性，其次在于该过程可通过反映感兴趣主题的用户自定义关键词进行引导。该系统可用于构建新的文献集合、更新和评估现有集合，以及利用收集到的文献识别该领域的相关专家。我们通过模拟会话评估了推荐方法，并开展了一项用户研究，调查该界面所支持的搜索策略和使用模式。
英文摘要：
Citations allow quickly identifying related research. If multiple publications are selected as seeds, specific 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 refines 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, first, the transparency of how the rankings are explained visually and, second, that the process can be steered through user-defined keywords, which reflect 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 field. We evaluated the recommendation approach through simulated sessions and performed a user study investigating search strategies and usage patterns supported by the interface.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
