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

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...

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Bibliographic Details
Published in:Knowledge-based systems 2024-11, Vol.303, p.112437, Article 112437
Main Authors: Santosa, Natasha Christabelle, Liu, Xin, Han, Hyoil, Miyazaki, Jun
Format: Article
Language:English
Subjects:
Attention mechanisms
Dynamic user interest
Graph neural networks
Recommender systems
Scientific paper recommendation
Sequential recommendation
Citations: Items that this one cites
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Summary: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., papers they cited in their past publication(s) or reading histories. In this paper, we propose a novel 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. In solving 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 and our proposed pipeline outperform existing standard RS baselines.
ISSN:0950-7051
DOI:10.1016/j.knosys.2024.112437