A multi-criteria hybrid citation recommendation system based on linked data

Citation recommendation systems can help a researcher find works that are relevant to his field of interest. Currently, most approaches in citation recommendation are based on a closed-world view which is limited to using a single data source for recommendation. Such a limitation decreases quality of the recommendations since no single data source contains all required information about different aspects of the literature. This paper proposes a citation recommendation approach based on the open-world view provided by the emerging web of data. It uses multiple linked data sources to create a rich background data layer, and a combination of content-based and multi-criteria collaborative filtering as the recommendation algorithm. Experiments demonstrate that the proposed approach is sound and promising.