PaperWeaver: Enriching Topical Paper Alerts by Contextualizing
  Recommended Papers with User-collected Papers
Yoonjoo Lee Hyeonsu B. Kang Matt Latzke Juho Kim Jonathan Bragg Joseph Chee Chang Pao Siangliulue
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.