Scientific publication recommendations based on collaborative citation networks

To learn about the state of the art for a research project, researchers must conduct a literature survey by searching for, collecting, and reading related scientific articles. Popular search systems, online digital libraries, and Web of Science (WoS) sources such as IEEE Explorer, ACM, SpringerLink, and Google Scholar typically return results or articles that are similar to keywords in the user's query. Some digital libraries also include content-based recommenders that suggest papers similar to one the user likes based on the contents of paper, i.e., the keywords it contains. In this work, we present a recommender module that suggests papers to users based on the seed paper's Citation Network. This work takes into account the combination of the co-citation and co-reference factors to improve algorithm's effectiveness. We applied and improved the the CCIDF (Common Citation Inverse Document Frequency) algorithm used by the CiteSeer digital library. This improved algorithm, called CCIDF+, was evaluated using data collected from Microsoft Academic Search (MAS). Experimental results show that CCIDF+ outperforms CCIDF.