Information retrieval (IR) models based on vector spaces have been investigated for a long time. Nevertheless, they have recently attracted much research interest. In parallel, context has been rediscovered as a crucial issue in information retrieval. This article presents a principled approach to modeling context and its role in ranking information objects using vector spaces. First, the article outlines how a basis of a vector space naturally represents context, both its properties and factors. Second, a ranking function computes the probability of context in the objects represented in a vector space, namely, the probability that a contextual factor has affected the preparation of an object.