A Cognitive Dissimilarity Measure for Information Retrieval Result Sets

Result rankings from context-aware information retrieval are inherently dynamic, as the same query can lead to significantly different outcomes in different contexts. For example, the search term Digital Camera will lead to different – albeit potentially overlapping – results in the contexts Customer Reviews and Shops, respectively. The comparison of such result rankings can provide useful insights into the effects of context changes on the information retrieval results. In particular, the impact of single aspects of the context in complex applications can be analyzed to identify the most (and least) influential context parameters. While a multitude of methods exists for assessing the relevance of a result ranking with respect to a given query, the question how different two result rankings are from a user’s point of view has not been tackled so far. This paper introduces DIR, a cognitively plausible dissimilarity measure for information retrieval result sets that is based solely on the results and thus applicable independently of the retrieval method. Unlike statistical correlation measures, this dissimilarity measure reflects how human users quantify the changes in information retrieval result rankings. The DIR measure supports cognitive engineering tasks for information retrieval, such as work flow and interface design: Using the measure, developers can identify which aspects of context heavily influence the outcome of the retrieval task and should therefore be in the focus of the user’s interaction with the system. The cognitive plausibility of DIR has been evaluated in two human participants tests, which demonstrate a strong correlation with user judgments.