SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships

  • Vu Le Anh
  • Vo Hoang Hai
  • Hung Nghiep Tran
  • Jason J. Jung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8733)


In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it combines four important factors in scientific publications: content, publicity, impact and randomness. Particularly, a recommendation system (called SciRecSys) has been presented to support users to efficiently find out relevant articles.


Keyword ranking Keyword similarity Keyword inference Scientific Recommendation System Bibliographical corpus 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Vu Le Anh
    • 1
    • 2
  • Vo Hoang Hai
    • 3
  • Hung Nghiep Tran
    • 4
  • Jason J. Jung
    • 5
  1. 1.Nguyen Tat Thanh UniversityHo Chi Minh cityVietnam
  2. 2.Big IoT BK Project TeamYeungnam UniversityGyeongsanKorea
  3. 3.Information Technology CollegeHo Chi Minh cityVietnam
  4. 4.University of Information TechnologyHo Chi Minh cityVietnam
  5. 5.Chung-Ang UniversitySeoulKorea

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