  Recommender systems learn about user preferences over time, automatically
finding things of similar interest. This reduces the burden of creating
explicit queries. Recommender systems do, however, suffer from cold-start
problems where no initial information is available early on upon which to base
recommendations. Semantic knowledge structures, such as ontologies, can provide
valuable domain knowledge and user information. However, acquiring such
knowledge and keeping it up to date is not a trivial task and user interests
are particularly difficult to acquire and maintain. This paper investigates the
synergy between a web-based research paper recommender system and an ontology
containing information automatically extracted from departmental databases
available on the web. The ontology is used to address the recommender systems
cold-start problem. The recommender system addresses the ontology's
interest-acquisition problem. An empirical evaluation of this approach is
conducted and the performance of the integrated systems measured.
