Library collections are all too often like icebergs: The amount exposed on the surface is only a fraction of the actual amount of content, and we’d like to recommend relevant items from deep within the catalog to users. With the assistance of an XSEDE Allocation grant (http://xsede.org), we’ve used R to reconstitute anonymous circulation data from the University of Illinois’s library catalog into separate user transactions. The transaction data is incorporated into subject analyses that use XSEDE supercomputing resources to generate predictive network analyses and visualizations of subject areas searched by library users using Gephi (https://gephi.org/). The test data set for developing the subject analyses consisted of approximately 38,000 items from the Literatures and Languages Library that contained 110,000 headings and 130,620 transactions. We’re currently working on developing a recommender system within VuFind to display the results of these analyses.