Modeling Information Flow In Dynamic Information Retrieval

User interaction with a dynamic information retrieval (DIR) system can be seen as a cooperative effort towards finding relevant information. In this cooperation, the user provides the system with evidence of his or her information need (e.g., in the form of queries, query reformulations, relevance judgments, or clicks). In turn, the system provides the user with evidence of the available information (e.g., in the form of a set of candidate results). Throughout this conversational process, both user and system may reduce their uncertainty with respect to each other, which may ultimately help in finding the desired information. In this paper, we present an information-theoretic model to quantify the flow of information from users to DIR systems and vice versa. By employing channels with memory and feedback, we decouple the mutual information among the behavior of the user and that of the system into directed components. As a result, we are able to measure: (i) by how much the DIR system is capable of adapting to the user; and (ii) by how much the user is influenced by the results returned by the DIR system. We discuss implications of the proposed framework for the evaluation and optimization of DIR systems.