Enhancing citation recommendation with various evidences

With the tremendous amount of citations available in digital library, how to suggest citations automatically, to meet the information needs of researchers has become an important problem. In this paper, we propose a model which treats citation recommendation as a special retrieval task to address this challenge. First, users provide a target paper with some metadata to our system. Second, the system retrieves a relevant candidate citation set. Then the candidate citations are reranked by well-chosen citation evidence, such as publication time preference, self-citation preference, co-citation preference and publication reputation preference. Especially, various measures are introduced to integrate the evidence. We experimented with the proposed model on an established bibliographic corpus-ACL Anthology Network, the results show that the model is valuable in practice, and citation recommendation can be significantly improved using proposed evidences.