The variety of user intents and interests is a key factor which makes information retrieval challenging, since textual queries, as a mostly adopted representation of information need, are often insufficient to describe what a user perceives as useful for his information goal. Other evidence can be adopted to complement this representation. But the utilization of multiple and diverse sources of evidence within a document ranking function increases the complexity of its design because of the diversity among sources. This talk illustrates a methodology which seamlessly describes the sources and the document ranking function in order to reduce this complexity. In particular the methodology models the contribution of diverse sources as vector subspaces and each document as a vector. The distance between the document vector and the subspace is adopted as measure of the degree to which the document satisfies the dimension of the information need representation corresponding to the considered source. Moreover, this talk discusses an utilization of the methodology to obtain and exploit a user behavior model built from the observed user interaction features for representing the user’s information need and accessed documents.