A Survey on Data Mining Techniques in Research Paper Recommender Systems

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Author(s): Benard Magara Maake (Tshwane University of Technology, South Africa), Sunday O. Ojo (Tshwane University of Technology, South Africa)and Tranos Zuva (Vaal University of Technology, South Africa)
Copyright: 2019
Pages: 25
Source title: Research Data Access and Management in Modern Libraries
Source Author(s)/Editor(s): Raj Kumar Bhardwaj (University of Delhi, India)and Paul Banks (The Royal Society of Medicine, UK)
DOI: 10.4018/978-1-5225-8437-7.ch006

Keywords: Information Science Reference / Library & Information Science / Library Science / Research Methods

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Abstract

In this chapter, the authors give an overview of the main data mining techniques that are utilized in the context of research paper recommender systems. These techniques refer to mathematical models and tools that are utilized in discovering patterns in data. Data mining is a term used to describe a collection of techniques that infer recommendation rules and build models from research paper datasets. The authors briefly describe how research paper recommender systems' data is processed, analyzed, and then, finally, interpreted using these techniques. They review different distance measures, sampling techniques, and dimensionality reduction methods employed in computing research paper recommendations. They also review the various clustering, classification, and association rule-mining methods employed to mine for hidden information. Finally, they highlight the major data mining issues that are affecting research paper recommender systems.

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A Survey on Data Mining Techniques in Research Paper Recommender Systems

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