SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships
This addresses the need for better keyword-based representation and recommendation in scientific literature, but appears incremental as it builds on existing Markov Chain methods.
The authors tackled the problem of discovering keyword relationships in scientific publications using a Markov Chain model, resulting in a recommendation system called SciRecSys that helps users find relevant articles efficiently.
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.