The introduction of the World Wide Web dramatically impacted our fundamental notion of information sharing, providing unparalleled awareness of both the power of information access and the penalty of information overload. Today’s research on Semantic Web techniques focuses on the next step, a Service Oriented Architecture supporting automated sharing of services as well as data. Personalized service/source recommendation tools, utilizing user preference data, would be extremely valuable in tailoring information access to the user. Much can be learned from the Recommender community about incorporating preference data into the retrieval process. However, it is critical that rigorous statistical techniques be maintained in combining results across data and service sources that are not under the control of a single developer. In this paper we explore the extension of nonparametric techniques to the development of Collaborative Recommenders and its impact on establishing a generalized recommendation service within a Service Oriented Architecture.