In this thesis, we develop a new framework for evaluating as well as optimizing retrieval systems with respect to their utility, which is measured in terms of relevance and novelty of information. We combine a nugget-based model of utility with a probabilistic model of user behavior; this leads to a flexible metric that generalizes existing evaluation measures. We demonstrate that our framework naturally extends to the evaluation of session-based retrieval while maintaining a consistent definition of novelty across multiple ranked lists. 
