A novel hybrid paper recommendation system using deep learning

Every year, thousands of papers are published in journals and conferences by researchers in many diferent felds. These papers are an important guide for other researchers. However, the increasing amount of digital data with the development of information technologies makes it difcult to reach the desired information. Recommendation systems play an important role in facilitating researchers’ access to studies on their subjects. It provides faster and easier access to papers on the desired subject. Recommendation systems are developed according to the user profle or subject. In this paper, a novel hybrid paper recommendation system based on deep learning is proposed. The method uses a combination of document similarity, hierarchical clustering, and keyword extraction. Our aim is to group papers in diferent felds such as computer science, economics, medicine, or in a specifc feld, according to their subjects, and to present papers with high semantic similarity to the user according to the query entered. The study has been applied on real dataset containing papers from diferent categories such as machine learning, artifcial intelligence, human–computer interaction in computer science. The success of each stage of the study has been evaluated separately. However, looking at the system as a whole, the overall performance of the proposed approach is 80%. Papers having high similarity with their queries have been recommended to users. Thus, access to the studies on the desired subject in the huge amount of papers has been made faster and easier.