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

TL;DR

This paper introduces SciRecSys, a recommendation system that leverages a Markov Chain model to discover relationships among keywords in scientific publications, enhancing the retrieval of relevant articles by considering content, publicity, impact, and randomness.

Contribution

It presents a novel Markov Chain-based approach for discovering keyword relationships and a recommendation system that integrates multiple factors for improved scientific article retrieval.

Findings

Effective keyword relationship discovery method

Improved article recommendation accuracy

Integration of content, publicity, impact, and randomness factors

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.