The cost structure of sensemaking
CHI '93 Proceedings of the INTERACT '93 and CHI '93 Conference on Human Factors in Computing Systems
Task complexity affects information seeking and use
Information Processing and Management: an International Journal
Information Processing and Management: an International Journal
ACM SIGIR Forum
A symbiotic theory formation system
A symbiotic theory formation system
Improving text categorization by resolving semantic ambiguity
Systems and Computers in Japan
The Turn: Integration of Information Seeking and Retrieval in Context (The Information Retrieval Series)
Journal of the American Society for Information Science and Technology
A Comparison of Pair Versus Solo Programming Under Different Objectives: An Analytical Approach
Information Systems Research
Impedance matching of humans * machines in high-Q information retrieval systems
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Artificial Intelligence and Law
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The objective of Search is to find documents relevant to a particular user's notion of relevance. However, relevance is often a moving target: imperfectly defined and subject to change as more documents are seen. In this paper, we report on systematic User Modeling (UM) and the use of a system-internal agent (proxy) to produce a hybrid human-computer system that achieves extraordinarily high performance on mediated Search tasks. We present details of our UM-approach and its four main components: (i) use case (ii) scope (iii) nuance and (iv) linguistic variability. We illustrate how these components provide a framework with which a user and a proxy co-construct a shared representation of information needs and mutual knowledge. This representation serves as the common ground through which external knowledge is shared, mediated, negotiated, synthesized and made accessible to the system. We evaluated the performance of our system on the Legacy Tobacco Documents Library, a corpus of advertising, manufacturing, marketing, sales and scientific research activities of major US tobacco companies. Independently adjudicated results from NIST's 2008 TREC legal track demonstrate that our approach to UM yields high performance on Search tasks.