Our framework provides a strong foundation for modeling retrieval performance in terms of non-independent utility of documents across multiple ranked lists. Moreover, it allows accurate evaluation and optimization of retrieval systems under realistic conditions, and hence, enable rapid development and tuning of new algorithms for novelty-based retrieval without the need for user-centric evaluations involving human subjects, which, although more realistic, are expensive, time-consuming, and risky in a live environment.
