A Fast and Comprehensive Literature Search Tool for Information Systems Researchers

For individual researchers, literature search has always been a tedious and time-consuming work, and it is often difficult to find a complete list of relevant articles using existing literature search engines. To address this problem, we propose a novel citation recommendation method using content and citation graph-based information, which produces a list of relevant references given the input of an abstract. In our method, we introduce a new feature of similar peers’ citation choices, which
captures the wisdom of crowds in the reference lists of academic articles. The proposed method has achieved better performance in the experiments on a standard dataset compared with existing method. To develop the literature search tool, we plan to first construct a dataset of the paper citation network within the three top IS journals (i.e., ISR, JMIS, MISQ). Then, we plan to implement the proposed method on ISTopic.org, an online platform for the exploration of research topics.