Tenti, P;
Thomas, J;
Peñaloza, R;
Pasi, G;
(2021)
Using an ensemble of features for personalized recommendations of scientific publications.
In:
(Proceedings) CEUR Workshop Proceedings.
CEUR-WS
(In press).
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Abstract
Maintaining reviews of scientific publications as soon as new relevant publications are available is a typical challenge to many research communities. We address this challenge as a content-based recommendation problem, where the publications already selected for a review drive the recommendation of the new publications. In addition, resources such as domain databases, ontologies and academic graphs provide structured information about publications (e.g., authors, journals, conferences). Our experiments show that a simple model based on that structured information to represent publications achieve high precision and recall, and outperform models that use more sophisticated representations based on embeddings.
| Type: | Proceedings paper |
|---|---|
| Title: | Using an ensemble of features for personalized recommendations of scientific publications |
| Event: | CEUR Workshop Proceedings |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | http://ceur-ws.org/ |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education UCL > Provost and Vice Provost Offices > School of Education > UCL Institute of Education > IOE - Social Research Institute |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10136206 |
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