While search expertise has long been considered an important variable in the study of online information search, the creation of a robust measure to characterize it has eluded researchers. Instead, those wishing to measure search expertise often include a single item that asks people to indicate how often they search or for how long they have been searching. In this paper, we report initial results of a four-phase study aimed at developing a measure of search expertise. Interviews and focus groups were conducted with nine professional searchers and an inventory of items related to online search expertise was created. While participants identified a number of usual items for determining search expertise, such as prior search experience, domain knowledge and resource knowledge, they also identified a large number of items related to personality characteristics such as persistence, flexibility, curiosity, adaptability and humility. These results suggest that measuring personality characteristics in addition to experience and knowledge might help determine who will be a more successful searcher. In today's search environments, where many people describe themselves as experts, representing these types of characteristics in a measure of search expertise might allow researchers and practitioners to observe more variation in populations of interest, design more effective instruction and make better predictions about search outcomes.

This paper evaluates undergraduate students' knowledge, interests and experiences with 20 topics from the TREC Robust Track collection. The goal is to characterize these topics along several dimensions to help researchers make more informed decisions about which topics are most appropriate to use in experimental IIR evaluations with undergraduate student subjects.

Query formulation is one of the most difficult and important aspects of information seeking and retrieval. Two techniques, term relevance feedback and query suggestion, provide methods to help users formulate queries, but each is limited in different ways. In this research we combine these two techniques by automatically creating query suggestions using term relevance feedback techniques. To evaluate our approach, we conducted an interactive information retrieval study with 55 subjects and 20 topics. Each subject completed four topics, half with a term suggestion system and half with a query suggestion system. We also investigated the source of the suggestions: approximately half of all subjects were provided with system-generated suggestions, while half were provided with user-generated suggestions. Results show that subjects used more query suggestions than term suggestions and saved more documents with these suggestions, even though there were no significant differences in performance. Subjects preferred the query suggestion system and rated it higher along a number of dimensions including its ability to help them think of new approaches to searching. Qualitative data provided insight into subjects' usage and ratings, and indicated that subjects often used the suggestions even when they did not click on them.