  Tools for filtering the World Wide Web exist, but they are hampered by the
difficulty of capturing user preferences in such a dynamic environment. We
explore the acquisition of user profiles by unobtrusive monitoring of browsing
behaviour and application of supervised machine-learning techniques coupled
with an ontological representation to extract user preferences. A multi-class
approach to paper classification is used, allowing the paper topic taxonomy to
be utilised during profile construction. The Quickstep recommender system is
presented and two empirical studies evaluate it in a real work setting,
measuring the effectiveness of using a hierarchical topic ontology compared
with an extendable flat list.
