Users often find difficult to navigate through a large information space to find the required information. One of the reasons is the difficulty in designing systems that would present the user with an optimal set of navigation options to support varying information needs. As a solution, this paper proposes a method referred as interaction portfolio theory. This theory is inspired by the economic theory called the “Modern Portfolio theory”, which offers users with optimal interaction options by taking into account user’s goal expressed via interaction during the task, but also the risk related to a potentially suboptimal choice made by the user. The proposed method learns the relevant interaction options from user behavior interactively and optimizes relevance and diversity to allow the user to accomplish the task in a shorter interaction sequence. This theory can be applied to any IR system to help users to retrieve the required information easily.