Finding information on a large web site can be a difficult and time-consuming process.
Recommender systems can help users find information by providing them with personalized
suggestions. In this paper the recommender system PRES is described that uses content-based
filtering techniques to suggest small articles about home improvements. A domain such as
this implicates that the user model has to be very dynamic and learned from positive feedback
only. The relevance feedback method seems to be a good candidate for learning such a user
model, as it is both efficient and dynamic.