A General Model for Mutual Ranking Systems

Vu Le AnhHai Vo HoangKien Le TrungHieu Le TrungJason J. Jung

Lecture Notes in Computer Science Volume 8397, 2014, pp 211-220

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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 nclasses of objects at once. The ranking scores of these classes are dependent to the others. For instance, PageRank by Google is a 2-linear mutual ranking, which ranks the webpages 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 models in which the citations are not considered. The experimental results are based on the DBLP dataset, which contains more than one million papers, authors and thousands of conferences and journals in computer science. Finally, N-star ranking is a very strong ranking algorithm can be applied in many real-world problems.

Keywords

  • N-star ranking
  • Markov chain
  • PageRank
  • Academic ranking
  • Conference ranking
  • Ranking algorithms
  • Prolific ranking
  • Recommendation systems
  • Bibliographical database
  • DBLP