Work

S3PaR: Section-based Sequential Scientific Paper Recommendation for paper writing assistance

Year: 2024

Type: article

Abstract: A scientific paper recommender system (RS) is very helpful for literature searching in that it (1) helps novice researchers explore their own field and (2) helps experienced researchers explore new fields outside their area of expertise. However, existing RSs usually recommend relevant papers based on users' static interests, i.e., their citation from past publication(s) or reading histories. We have therefore come up with a unique recommendation task based on users' dynamic interests during their paper-writing activity. This dynamism is revealed in (for example) the topic shift while writing the introduction vs. related works section. To implement this task, we developed a new pipeline called Section-based Sequential Scientific Paper Recommendation (S3PaR), which recommends papers based on the context of the given user's currently written paper section. Our experiments demonstrate that this unique task is feasible and that our proposed pipeline outperforms existing standard RS baselines.

Source: Knowledge-Based Systems

DOI: 10.1016/j.knosys.2024.112437

Authors: Natasha Christabelle Santosa, Xin Liu, Hyoil Han, Jun Miyazaki

Institutions: Tokyo Institute of Technology, Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Illinois State University

Language: English

Topic: Recommender Systems and Techniques

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,250

Funders: Japan Society for the Promotion of Science, Japan Science and Technology Agency, Core Research for Evolutional Science and Technology, Exploratory Research for Advanced Technology