- Yiming Yang, A. Lad. 2011. A framework for evaluation and optimization of relevance and novelty-based retrieval. Abstract: There has been growing interest in building and optimizing retrieval systems with respect to relevance and novelty of information, which together more realistically reflect the usefulness of a system as perceived by the user. How to combine these criteria into a single metric that can be used to measure as well as optimize retrieval systems is an open challenge that has only received partial solutions so far. Unlike relevance, which can be measured independently for each document, the novelty of a document depends on other documents seen by the user during his or her past interaction with the system. This is especially problematic for assessing the retrieval performance across multiple ranked lists, as well as for learning from user's feedback, which must be interpreted with respect to other documents seen by the user. Moreover, users often have different tolerances towards redundancy depending on the nature of their information needs and available time, but this factor is not explicitly modeled by existing approaches for novelty-based retrieval. 
