Many problems in information access require understanding deeply how humans interact with and can exploit information environments. Unlike classical HCI, much of the interaction is at the level of the information itself, rather than at the level of the device, and the semantic content of the information is hugely important. Unlike classical IR, time deadlines, interpretation, and the use of the information retrieved are dominating. Important decisions need to be made on massive data with high uncertainly under deadline pressure. More research is needed starting from the point of view of the user and the structure of the task.
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Traditional Graphical User Interface (GUI) techniques forces users to use Windows, Icons, Menus, and Pointing device to interact with the computer. User interfaces for these are largely derived from the standard overlapped-window Xerox PARC model of 25 years ago or before and reflect the constraints of the time, whereas computer processors and memories have advanced by four orders of magnitude and the understanding of the human cognition, human-machine communications, and neuroscience have also advanced.
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The goal of user modeling is to produce highly relevant information ecologies on a per user basis. Leveraging our existing technologies in instrumentation, content analysis, linguistics, and task analysis, we are developing a user-modeling infrastructure that tackles the challenging problems of modeling content consumption as well as sensemaking tasks.
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Our applications are used in various solutions.
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We are building a program of research with three basic aims: the first aim is to build a supporting science of Human-Information Interaction (HII). This would include a program of empirical analysis of end-users, their tasks, and their corpora. The second aim is to build a set of prototype systems that implement some of the emerging ideas, including experiments to evaluate these systems. The third aim is to evolve a set of underlying enabling technologies as required to support the first two.