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

Vu Le Anh, Vo Hoang Hai, Hung Nghiep Tran, Jason J. Jung

Computational Collective Intelligence. Technologies and Applications Volume 8733 of the series Lecture Notes in Computer Science pp 72-82

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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