Abstract
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.
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Le Anh, V., Hoang Hai, V., Tran, H.N., Jung, J.J. (2014). SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships. In: Hwang, D., Jung, J.J., Nguyen, NT. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2014. Lecture Notes in Computer Science(), vol 8733. Springer, Cham. https://doi.org/10.1007/978-3-319-11289-3_8
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DOI: https://doi.org/10.1007/978-3-319-11289-3_8
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