ComLittee: Literature Discovery with Personal Elected Author Committees

要旨

In order to help scholars understand and follow a research topic, significant research has been devoted to creating systems that help scholars discover relevant papers and authors. Recent approaches have shown the usefulness of highlighting relevant authors while scholars engage in paper discovery. However, these systems do not capture and utilize users’ evolving knowledge of authors. We reflect on the design space and introduce ComLittee, a literature discovery system that supports author-centric exploration. In contrast to paper-centric interaction in prior systems, ComLittee’s author-centric interaction supports curating research threads from individual authors, finding new authors and papers using combined signals from a paper recommender and the curated authors’ authorship graphs, and understanding them in the context of those signals. In a within-subjects experiment that compares to a paper-centric discovery system with author-highlighting, we demonstrate how ComLittee improves author and paper discovery.

著者
Hyeonsu B. Kang
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Nouran Soliman
Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
Matt Latzke
Allen Institute for AI, Seattle, Washington, United States
Joseph Chee Chang
Semantic Scholar, Seattle, Washington, United States
Jonathan Bragg
Allen Institute for Artificial Intelligence, Seattle, Washington, United States
論文URL

https://doi.org/10.1145/3544548.3581371

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Tools for data scientists and Literature Reviews

Hall A
6 件の発表
2023-04-25 23:30:00
2023-04-26 00:55:00