Author's paper Similarity Prediction based on the Similarity of Textual References to Visual Features

Abstract

In this paper we introduce a mechanism to find similar papers of an author, based on the author's previous1 publications. In other words, since the author(s) of a paper are more likely to publish similar work(s) to their paper, we use this intuition to seek related papers based on the visual similarity of those papers. The visuality here is the figures and or tables that are commonly used by authors to describe their method structure and/or the result of their experiments. Since similar works of authors are focused on solving similar problems as well as developing and improving similar techniques, we noticed that comparing these visual features among their publications would help to spot most similar papers of those authors. We call our method, Similarity of Textual References to Visual Features which means, we compare parts of content of any two arbitrary papers that have references to any figures and/or tables. In our experiment we show that how we can use this similarity together with other factors of a paper to form a Boolean function which helps to build an indexation for papers based on the number of their authors. In this way, we omit time consuming process of papers' content determined analysis, such as, textual content analysis, building coauthor network, citation network etc. In addition, our Boolean function has the ability of adjusting level of Sensitivity. If we want to achieve higher accuracy of similar papers, the Boolean function needs to be enabled for more2 conditions.