Augmenting context-aware citation recommendations with citation and co-authorship history

With the increasing number of research papers being published, it has become a challenge to search for the most suitable articles for accurate referencing. Many local citation recommendation systems have begun to locate the suitable candidates by using the texts accompanying the citation suffix, along with the metadata of the target documents. Previous research has shown the positive effects from the citation relationships on such recommendations, however, the influence from the co-authorship history has not been fully investigated. In this paper, we extend the model proposed by Jeong, Jang & Park (2020) by combining the context, citation history with co-authorship information into the recommendation system. We also propose to use more domain-specific embeddings to better capture the semantics in the context. Our experiments show the positive effect of co-authorship information on citation recommendations, and that our model based on the combination of domain-specifically embedded context, the citation and the co-authorship history significantly outperforms the basic context-based recommendation model.