Offline evaluation of information retrieval systems typically focuses on a single effectiveness measure that models the utility for a typical user. Such a measure usually combines a behavior-based rank discount with a notion of document utility that captures the single relevance criterion of topicality. However, for individual users relevance criteria such as credibility, reputability or readability can strongly impact the utility. Also, for different information needs the utility can be a different mixture of these criteria. Because of the focus on single metrics, offline optimization of IR systems does not account for different preferences in balancing relevance criteria.

We propose to mitigate this by viewing multiple relevance criteria as objectives and learning a set of rankers that provide different trade-offs w.r.t. these objectives. We model document utility within a gain-based evaluation framework as a weighted combination of relevance criteria. Using the learned set, we are able to make an informed decision based on the values of the rankers and a preference w.r.t. the relevance criteria. On a dataset annotated for readability and a web search dataset annotated for sub-topic relevance we demonstrate how trade-offs between can be made explicit. We show that there are different available trade-offs between relevance criteria.