Can collaborative filtering be successfully applied to digital libraries in a manner that improves the effectiveness of the library? Collaborative filtering systems remove the limitation of traditional content-based search interfaces by using individuals to evaluate and recommend information. We introduce an approach where a digital library user specifies their need in the form of a question, and is provided with recommendations of documents based on ratings by other users with similar questions. Using a testbed of the Tsunami Digital Library, we found evidence that suggests that collaborative filtering may decrease the number of search queries while improving users' overall perception of the system. We discuss the challenges of designing a collaborative filtering system for digital libraries and then present our preliminary experimental results.