In electronic commerce applications, prospective buyers may be interested in receiving recommendations to assist with their purchasing decisions. Previous research has described two main models for automated recommender systems - collaborative filtering and the knowledge-based approach. In this paper, we present an architecture for designing a hybrid recommender system that combines these two approaches. We then discuss how such a recommender system can switch between the two methods, depending on the current support for providing good recommendations from the behaviour of other users, required for the collaborative filtering option. We also comment on how the overall design is useful to support recommendations for a variety of product areas and present some directions for future work.