A Survey on Data Mining Techniques in Research Paper Recommender Systems

Abstract

In this chapter, we give an overview of the main data mining techniques that are utilised in the context of research paper recommender systems. These techniques refer to mathematical models and tools that are utilised 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. We briefly describe how research paper recommender systems’ data is processed, analysed and then finally interpreted using these techniques. We review different distance measures, sampling techniques and dimensionality reduction methods employed in computing research paper recommendations. We also review the various clustering, classification and association rule mining methods employed to mine for hidden information. Finally, we highlight the major data mining issues that are affecting research paper recommender systems.