Next, we address the complementary problem of optimization, i.e., how to maximize retrieval performance for one or more ranked lists with respect to the proposed measure. Since the system does not have knowledge of the nuggets that are relevant to each query, we propose a ranking approach based on the use of observable query and document features ( e.g., words and named entities) as surrogates for the unknown nuggets, whose weights are automatically learned from user feedback. However, finding the ranked list that maximizes the coverage of a given set of nuggets leads to an NP-hard problem. We take advantage of the sub-modularity of the proposed measure to derive lower bounds on the performance of approximate algorithms, and also conduct experiments to assess the empirical performance of a greedy algorithm under various conditions. 
