Opportunistic software composition of services is a novel interactive approach for the construction of software in open and dynamic ambient environments. The goal is to dynamically provide relevant applications to a user without predefined assembly plan or functional requirements. For that, an intelligent composition system builds, through distributed and interactive reinforcement learning, assemblies of software components present in the user's environment. A current limit of this approach is that in some situations, for example at startup, the composition engine lacks information and as a result proposes random assemblies to the user. The contribution discussed in this paper assists the engine in such situations by adding a feature model generated from the ambient environment. Thus, the engine gathers additional knowledge comparing its proposition to this feature model, providing more pertinent assemblies to the user.