Scholarly Paper Recommendation Datasets

Much of the world's new knowledge today is now largely captured in digital form and archived within a digital library system. However, these trends lead to information overload, where users find an overwhelmingly large number of publications that match their search queries but are largely irrelevant to their latent information needs. We address this problem by providing recommendation results by using latent information about the user's research interests that exists in their publication list (see the papers in ``Publications'' below for further details). We have released experimental datasets used in our papers. If you are interested in recommendation of scholarly papers, please try our dataset for your experiments! You can also use our datasets (especially, candidate papers to recommend) for other purposes such as classification, clustering, trend analysis, and so on.