With the amount of digitalized documents increasing exponentially, it is more difficult for users to keep up to date with the knowledge in their domain. In this paper, we present a framework named IFME (Information Filtering by Multiple Examples) in a digital library environment to help users identify the literature related to their interests by leveraging the Positive Unlabeled learning (PU learning). Using a few relevant documents provided by a user and considering the documents in an online database as unlabeled data (called U), it ranks the documents in U using a PU learning algorithm. From the experimental results, we found that while the approach performed well when a large set of relevant feedback documents were available, it performed relatively poor when the relevant feedback documents were few. We improved IFME by combining PU learning with under-sampling to tune the performance. Using Mean Average Precision (MAP), our experimental results indicated that with under-sampling, the performance improved significantly even when the size of P was small. We believe the PU learning based IFME framework brings insights to develop more effective digital library systems.