A Framework for paper submission recommendation system
Cuong, Dinh Viet, Nguyen, Dac, Huynh, Son, Huynh, Phong, Gurrin, CathalORCID: 0000-0003-2903-3968, Dao, Minh-Son, Dang-Nguyen, Duc-TienORCID: 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
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
Proceedings of the 2020 International Conference on Multimedia Retrieval (ICMR'20).
.
Association for Computing Machinery (ACM). ISBN 978-1-4503-7087-5