Recommender systems combine ideas from information retrieval, machine learning and user profiling research in order to provide end-users with more proactive and personalized information information retrieval applications. Two popular approaches have come to dominate. Content-based techniques leverage the availability of rich item descriptions to identify new items that are similar to those that a user has liked in the past. In contrast, collaborative filtering techniques rely on the availability of user profiles in which sets of items have been rated. They recommend new items to a target user on the basis that similar users have preferred these items in the past. In this paper we will present two case-studies of how association rule mining techniques have been used to significantly enhance the power of content-based and collaborative filtering recommender systems.