In a world where a person's number of choices can be overwhelming, recommender systems help users find and evaluate items of interest. They do so by connecting users with information regarding the content of recommended items or the opinions of other individuals. Such systems have become powerful tools in domains such as electronic commerce, digital libraries and knowledge management. The authors address such systems, as well as a relatively new class of recommender system called meta-recommenders. Meta-recommenders provide users with personalised control over the generation of a single recommendation list formed from a combination of rich data using multiple information sources and recommendation techniques. They discuss observations made from the public trial of a meta-recommender system in the domain of movies and lessons learned from the incorporation of features that allow persistent personalisation of the system. Finally, they consider the challenges of building real-world, usable meta-recommenders across a variety of domains.