A text recommender system recommends sets of documents for individual users on the basis of user models, which are incrementally constructed given feedback on previous recommendations. Users are reluctant to take the time to provide such feedback explicitly. One of the contributions of this research is an interface design for a recommender system which infers document preferences by monitoring users’ actions. A second problem for recommender systems is determining the composition of a set of recommendations, especially when users have many interests. The interface presented provides a mechanism for users to define multiple topics of interest and control the proportions between them. Observations from initial usability tests are encouraging---they demonstrate the system successfully learning multi-topic user profiles using only the implicit feedback of users’ clicking and drag-and-drop actions.