PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers

ArXiv Pub Date : 2024-03-05 DOI:10.1145/3613904.3642196
Yoonjoo Lee, Hyeonsu B Kang, Matt Latzke, Juho Kim, Jonathan Bragg, Joseph Chee Chang, Pao Siangliulue
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Abstract

With the rapid growth of scholarly archives, researchers subscribe to"paper alert"systems that periodically provide them with recommendations of recently published papers that are similar to previously collected papers. However, researchers sometimes struggle to make sense of nuanced connections between recommended papers and their own research context, as existing systems only present paper titles and abstracts. To help researchers spot these connections, we present PaperWeaver, an enriched paper alerts system that provides contextualized text descriptions of recommended papers based on user-collected papers. PaperWeaver employs a computational method based on Large Language Models (LLMs) to infer users' research interests from their collected papers, extract context-specific aspects of papers, and compare recommended and collected papers on these aspects. Our user study (N=15) showed that participants using PaperWeaver were able to better understand the relevance of recommended papers and triage them more confidently when compared to a baseline that presented the related work sections from recommended papers.
PaperWeaver:通过将推荐论文与用户收集的论文联系起来,丰富专题论文提醒功能
随着学术档案的快速增长,研究人员订阅了 "论文提醒 "系统,该系统会定期向他们推荐最近发表的、与以前收集的论文相似的论文。然而,由于现有系统只提供论文标题和摘要,研究人员有时很难理解推荐论文与其自身研究背景之间的细微联系。为了帮助研究人员发现这些联系,我们推出了 PaperWeaver,这是一个丰富的论文提醒系统,它能根据用户收集的论文提供推荐论文的上下文文本描述。PaperWeaver 采用一种基于大型语言模型(LLM)的计算方法,从用户收集的论文中推断用户的研究兴趣,提取论文的特定上下文,并就这些方面对推荐论文和收集的论文进行比较。我们的用户研究(N=15)显示,使用PaperWeaver的参与者能够更好地理解推荐论文的相关性,并在与展示推荐论文中相关工作部分的基线相比时更有信心地对其进行分流。
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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