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IR evaluation methods for retrieving highly relevant documents
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
SearchPad: explicit capture of search context to support Web search
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Information access in context: experiences with the watson system
Information access in context: experiences with the watson system
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Modeling task-genre relationships for IR in the workplace
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Connecting topics in document collections with stepping stones and pathways
Proceedings of the 14th ACM international conference on Information and knowledge management
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Proceedings of the 14th ACM international conference on Information and knowledge management
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Proceedings of the 14th international conference on Intelligent user interfaces
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Data transformations and representations for computation and visualization
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Information at your fingertips: contextual IR in enterprise email
Proceedings of the 16th international conference on Intelligent user interfaces
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We are building an intelligent information system to aid users in their investigative tasks, such as detecting fraud. In such a task, users must progressively search and analyze relevant information before drawing a conclusion. In this paper, we address how to help users find relevant informa-tion during an investigation. Specifically, we present a novel approach that can improve information retrieval by exploiting a user's investigative context. Compared to existing retrieval systems, which are either context insensitive or leverage only limited user context, our work offers two unique contributions. First, our system works with users cooperatively to build an investigative context, which is otherwise very difficult to capture by machine or human alone. Second, we develop a context-aware method that can adaptively retrieve and evaluate information relevant to an ongoing investigation. Experiments show that our approach can improve the relevance of retrieved information significantly. As a result, users can fulfill their investigative tasks more efficiently and effectively.