Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Nov 28, 2018
Open Peer Review Period: Dec 1, 2018 - Jan 26, 2019
Date Accepted: Apr 19, 2019
(closed for review but you can still tweet)
Pubmender: Deep Learning Based Recommender System for Biomedical Publication Venue
ABSTRACT
Background:
It is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose a most suitable publication venue given the exponential growth of journals and conferences. Although recommender systems have achieved success in promoting movies, music and products, very few studies have explored recommendation of publication venues, especially for biomedical research. No recommender system exists that can specifically recommend journals in PubMed, the largest collection of biomedical literature.
Objective:
We proposed a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper’s abstract.
Methods:
In Pubmender, pretrained word2vec is first used to construct the start-up feature space. Then, a deep convolutional neural network is constructed to achieve the high-level representation of abstracts, and a fully connected softmax model is adopted to recommend the best journals.
Results:
We collected 880,165 papers from 1,130 journals in PubMed Central (PMC) and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkleon on the Microsoft Academic Search engine (MAS), a collaborative-filtering-based recommender system on the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top-10 recommendtations to be 87.0%, 22.9% and 196.0% higher than those of MAS, ACM, and CiteSeer, respectively. In addition, we compared our system with Journal Finder and Journal Suggester, which are tools of Elsevier and Springer to help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than Journal Finder and 406% higher than Journal Suggester for the top-10 recommendations. Our web service is freely available at http://www.keaml.cn:8081/.
Conclusions:
Our deep learning based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians to choose suitable venues for their papers.
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.