The task of recommending documents to knowledge workers differs from the task of recommending products to consumers. Variations in search context can undermine the effectiveness of collaborative approaches, while many knowledge workers function in an environment in which the open sharing of information may be impossible or undesirable. There is also the cold start problem of how to bootstrap a recommendation system in the absence of any usage statistics. We describe a system called ResultsPlus, which uses a blend of information retrieval and machine learning technologies to recommend secondary materials to attorneys engaged in primary law research. Rankings of recommended material are subsequently enhanced by incorporating historical user behavior and document usage data.