In this paper, we present an initial implementation with experimental results of online profile learning in Argo+, a framework for crowdsourcing task routing characterized by i) feature-based representation of both tasks and workers, and ii) learning techniques inspired to Rocchio relevance feedback for prediction of the most appropriate task to execute by a given worker