scholarly journals The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study

10.2196/12957 ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. e12957 ◽  
Author(s):  
Xiaoyue Feng ◽  
Hao Zhang ◽  
Yijie Ren ◽  
Penghui Shang ◽  
Yi Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Recommender System ◽  
Validation Study ◽  
Biomedical Publication ◽  
Development And Validation

scholarly journals The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study (Preprint)

2018 ◽  
Author(s):  
Xiaoyue Feng ◽  
Hao Zhang ◽  
Yijie Ren ◽  
Penghui Shang ◽  
Yi Zhu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Recommender System ◽  
Feature Space ◽  
Biomedical Literature ◽  
Computing Machinery ◽  
Start Up ◽  
Biomedical Publication ◽  
High Level ◽  
Fully Connected ◽  
Development And Validation

BACKGROUND It is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose the 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 aimed to propose a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper’s abstract. METHODS In Pubmender, pretrained word2vec was first used to construct the start-up feature space. Subsequently, a deep convolutional neural network was constructed to achieve a high-level representation of abstracts, and a fully connected softmax model was adopted to recommend the best journals. RESULTS We collected 880,165 papers from 1130 journals in PubMed Central and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkle on the Microsoft Academic Search (MAS) engine, a collaborative filtering–based recommender system for the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top 10 recommendations to be 87.0%, 22.9%, and 196.0% higher than that 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, respectively, that help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than that of Journal Finder and 406% higher than that of Journal Suggester for the top 10 recommendations. Our web service is freely available at https://www.keaml.cn:8081/. CONCLUSIONS Our deep learning–based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians choose suitable venues for their papers.

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