Hybrid-based Research Article Recommender System

Authors

  • Su-Anne Teh Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
  • Su-Cheng Haw Faculty of Computing and Informatics, Multimedia University, 63100, Cyberjaya, Malaysia
  • Heru Agus Santoso Faculty of Computer Science, Dian Nuswantoro University, Semarang, Indonesia

DOI:

https://doi.org/10.15379/ijmst.v10i2.1830

Keywords:

Research Article Recommender System, Recommender System, Content-Based Filtering, Collaborative Filtering, Hybrid Filtering.

Abstract

A recommender system, which might assist in providing clients with new information and a better experience, is becoming increasingly popular in this era of modernization. Recommender systems are often used by various platforms to provide new products to consumers, which may also help in improving product sales. Additionally, the recommender system is essential in academic domains. It is common for users to take a while to find and access the materials they need. The recommender system is now available, which could reduce the time spent looking for materials and improve student achievement. Therefore, it is crucial to explore more on the theory and implementation of the recommender system. This paper aims to study a few types of recommender system techniques and implement it in the research article recommender system. Additionally, related research on each of the three recommender systems will be reviewed, along with a description of the related study, the dataset used, and the evaluation method.

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Published

2023-09-05

How to Cite

[1]
S.-A. . Teh, S.-C. . Haw, and H. A. . Santoso, “Hybrid-based Research Article Recommender System ”, ijmst, vol. 10, no. 2, pp. 1587-1606, Sep. 2023.