Author-Profile-Based Journal Recommendation for a Candidate Article: Using Hybrid Semantic Similarity and Trend Analysis

Abstract

Finding the right journal for a manuscript to be submitted is difficult and often time-consuming because authors take into account some criteria while searching for the appropriate journal for their manuscript. One of the most important criteria is the content similarity of the journals and manuscript. For this purpose, the subject of the manuscript should be in accordance with the scope of the journal. Also, the manuscript content should be closed to the journals’ trend for higher chance of acceptance. Second criterion is to take into account the impact-factor, acceptance-rate, review-time and publishing houses of the journal, which are suitable for the author’s past publication profile. In this study, a novel method is proposed in which both the content of the article and the author / authors profile are considered together to find the appropriate journal. To the best of our knowledge, this is the first effort in this direction. Experimental results conducted on real data sets have shown that the proposed method is applicable and performs high accuracy values.

Keywords

• Journal suggester
• ontological similarity
• trend analyses
• venue selection
• user-profile recommender