Recommendation of research papers in DBpia: A Hybrid approach exploiting content and collaborative data
Yeon-Chang Lee, Jungwan Yeom, Kiburm Song, Jiwoon Ha, Kichun Lee, Jangho Yeo, Sang‐Wook Kim · 2016
DBpia is the largest digital-bibliography service provider in Korea. It provides several convenience functions for researchers. DBpia users (i.e., researchers) can search for papers via several search routes such as publications, publishers, authors, and keywords. Although the researchers can exploit the search functions, they may still have a number of search results as candidate papers to read. Therefore, it is crucial to provide a function of recommending most relevant papers to an individual user. In this paper, we (1) discuss several methods with four datasets of DBpia in the context of paper recommendation using content-based or graph-based recommendation, and (2) propose a hybrid approach suitable for paper recommendation combining the content-based and the graph-based approaches. We lastly conduct extensive experiments by a real-world academic literature dataset in DBpia to verify the effectiveness of our proposed approach.