As the number of people who use scientific literature databases has grown, the demand for literature retrieval services has steadily increased. One of the most popular retrieval service methods is to find a set of papers similar to the paper under consideration, which requires a measure that computes the similarities between the papers. Scientific literature databases exhibit two interesting characteristics that are not found in general databases. First, the papers cited by older papers are often not included in the database due to technical and economic reasons. Second, since a paper references previously published papers, few papers cite recently published papers. These two characteristics cause all existing similarity measures to fail in at least one of the following cases: (1) measuring the similarity between old, but similar papers, (2) measuring the similarity between recent, but similar papers, and (3) measuring the similarity between two similar papers: one old, the other recent. In this paper, we propose a new link-based similarity measure called C-Rank, which uses both in-link and out-link references, disregarding the direction of the references. In addition, we discuss the most suitable normalization method for scientific literature databases and we propose an evaluation method for measuring the accuracy of similarity measures. For the experiments, we used real-world papers from DBLP's database with reference information crawled from Libra. We then compared the performance of C-Rank with that of existing similarity measures. Experimental results showed that C-Rank achieved a higher accuracy than existing similarity measures.
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Theoretical Computer Science
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence