Recommender systems seek to predict the interest a user would find in an item, person or social element they had not yet considered, based upon the properties of the item, the user's past experience and similar users. However, recommended items are often presented to the user with no context and no ability to influence the results. We present a novel visualization technique for recommender systems in which, a user can see the items recommended for him, and understand why they were recommended. Focusing on a user, we render a planar visualization listing a set of recommended items. The items are organized such that similar items reside nearby on the screen, centered around realtime generated categories. We use a combination of iconography, text and tag clouds, with maximal use of screen real estate, and keep items from overlapping to produce our results. We apply our visualization to expert relevance maps in the enterprise and a book recommendation system for consumers. The latter is based on Shelfari, a social network for reading and books.