The way the users interact with Information Retrieval (IR) systems is an interesting topic of interest in the field of Human Computer Interaction (HCI) and IR. With the ever increasing information in the web, users are often lost in the vast information space. Navigating in the complex information space to find the required information, is often an abstruse task by users. 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 to the navigation problem, in this thesis we propose a method referred as interaction portfolio theory, based on Markowitz's 'Modern Portfolio theory', a theory of finance. It provides the users with N optimal interaction options in each iteration, 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. In each iteration, the proposed method learns the relevant interaction options from user behaviour 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 efficiently.
