Deep learning for journal recommendation system of research papers

Abstract

Many journals belonging to different publishers have emerged with the advancements in research. The increase in the number of scholarly journals has made it difficult for researchers to choose the correct journal for publishing their articles. Submitting an article to the correct journal is very important in terms of academic sharing and for shortening the publication time of the article. It is time consuming to determine the most suitable journal in scope among thousands of journal choices for the user. Therefore, journal recommendation systems have been an important tool for researchers. Recommendation systems generally depend on the user's publications, relationships with other authors, etc. The fact that it is based on features makes it not useful for users who are new to the research field. In this study, an approach that recommends a journal is proposed by using the title, abstract, keyword and reference information of the article, without the need of users' information. Unlike other studies, the scope information of the journals is needed to determine the appropriate journals for the article, which is usually obtained from the articles previously published in the related journals. The publications of the journals in the last 3 years have been used to determine the scope of the journal. Unlike the publishers' journal recommendation systems developed so far, this study is a comprehensive recommendation system that includes journals from more than one publisher. In this approach, SBERT has been used to find the similarity of the scope of journals with articles. When the results are compared with the Word2vec, Glove and FastText, which are often the preferred methods in document similarity, it was observed that sentence-level similarity-based recommendations with SBERT are more successful. The experimental results show the effectiveness of our approach.