Academic Articles Recommendation Using Concept-Based Representation

Abstract

Academic articles recommendation systems have gained a lot of interest as an effective tool to suggest relevant articles for researchers according to their interests. Explicit identification of the topics of interests from the contents of academic articles that the researchers have authored, downloaded or read has been always a challenging task. In this paper, we propose a concept-based method to represent researchers’ interests where the concepts generation process depends on the semantics of the words in the articles related to the researcher. The evaluation results show that the proposed method outperforms the recommendation baseline methods and produces better recommendations for researchers.