In this study, we develop a fast and comprehensive literature search tool for IS community. We first propose a novel citation recommendation method, which produces a list of relevant references given the input of a long query. In our method, we introduce a new feature of aggregate likelihood being cited, which captures the wisdom of crowds in the reference lists of academic articles, in addition to the topical similarity. The method has achieved better efficiency and accuracy on a standard dataset compared with existing methods. Next, we construct a citation network within the three top IS journals (i.e., ISR, JMIS, and MISQ). Finally, we plan to implement the proposed method on ISTopic.org, an online platform for the exploration of research topics. To further evaluate the performance of the literature search tool, we plan to conduct a user study to compare the usability of our tool with existing literature search tools.