The information explosion in today’s electronic world has created the need for information filtering techniques that help users filter out extraneous content to identify the right information they need to make important decisions. Recommender systems are one approach to this problem, based on presenting potential items of interest to a user rather than requiring the user to go looking for them. In this paper, we propose a recommender system that recommends research papers of potential interest to authors known to the CiteSeer database. For each author participating in the study, we create a user profile based on their previously published papers. Based on similarities between the user profile and profiles for documents in the collection, additional papers are recommended to the author. We introduce a novel way of representing the user profiles as trees of concepts and an algorithm for computing the similarity between the user profiles and document profiles using a tree-edit distance measure. Experiments with a group of volunteers show that our concept-based algorithm provides better recommendations than a traditional vector-space model based technique.