Evaluating scientific publications by \(N\)-linear ranking model

Abstract. Ranking has been applied in many domains using recommendation systems such as search engine, e-commerce, and so on. We will introduce and study N-linear mutual ranking, which can rank \(n\) classes of objects at once. The ranking scores of these classes are dependent to the others. For instance, PageRank by Google is a 2-linear ranking model, which ranks the web-pages and links at once. Particularly, we focus to \(N\)-star ranking model and demonstrate it in ranking conference and journal problems. We have conducted the experiments for the proposed models to classical ones. The experiments are based on the DBLP dataset, which contains more than one million papers, authors and thousands of conferences and journals in computer science. The experimental results show that \(N\)-star ranking model evaluates everything much more detail based on the context of their relationships.