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A Framework for paper submission recommendation system

Cuong, Dinh Viet, Nguyen, Dac, Huynh, Son, Huynh, Phong, Gurrin, Cathal orcid logoORCID: 0000-0003-2903-3968, Dao, Minh-Son, Dang-Nguyen, Duc-Tien orcid logoORCID: 0000-0002-2761-2213 and Nguyen, Binh T. (2020) A Framework for paper submission recommendation system. In: International Conference on Multimedia Retrieval (ICMR'20), 26–29 Oct 2020, Dublin, Ireland. ISBN 978-1-4503-7087-5

Abstract
Nowadays, recommendation systems play an indispensable role in many fields, including e-commerce, finance, economy, and gaming. There is emerging research on publication venue recommendation systems to support researchers when submitting their scientific work. Several publishers such as IEEE, Springer, and Elsevier have implemented their submission recommendation systems only to help researchers choose appropriate conferences or journals for submission. In this work, we present a demo framework to construct an effective recommendation system for paper submission. With the input data (the title, the abstract, and the list of possible keywords) of a given manuscript, the system recommends the list of top relevant journals or conferences to authors. By using state-of-the-art techniques in natural language understanding, we combine the features extracted with other useful handcrafted features. We utilize deep learning models to build an efficient recommendation engine for the proposed system. Finally, we present the User Interface (UI) and the architecture of our paper submission recommendation system for later usage by researchers.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:deep learning; recommendation system; paper submission
Subjects:UNSPECIFIED
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > ADAPT
Published in: Proceedings of the 2020 International Conference on Multimedia Retrieval (ICMR'20). . Association for Computing Machinery (ACM). ISBN 978-1-4503-7087-5
Publisher:Association for Computing Machinery (ACM)
Official URL:https://doi.org/10.1145/3372278.3391929
Copyright Information:© 2020 The Authors.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:Science Foundation Ireland under grant number SFI/13/RC/2106, L. Meltzers Høyskolefonds, UiB 2019/2259-NILSO
ID Code:24635
Deposited On:17 Jun 2020 13:24 by Cathal Gurrin . Last Modified 10 Mar 2023 13:25
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