Human beings, not machines, are the ultimate experts for information retrieval tasks, including recommender systems. Consequently, computers are most useful when they combine information about people’s judgments. Collaborative filtering systems make use of this observation by having users explicitly rate items, such as Web pages, with the system making recommendations to other users based on overlapping areas of interest. A disadvantage of collaborative filtering, at least as currently implemented, is that it depends on users’ explicitly entering data, which can be inconvenient and timeconsuming. We describe a recommender system, which we call ParaSite, that works by mining publicly-available hyperlinks on the Web, producing results competitive with the best text-based system.