Aki Reijonen

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We introduce interactive intent modeling, where the user directs exploratory search by providing feedback for estimates of search intents. The estimated intents are visualized for interaction on an Intent Radar, a novel visual interface that organizes intents onto a radial layout where relevant intents are close to the center of the visualization and(More)
[1] We have compiled results obtained from four high sedimentation rate hemipelagic sequences from the Celtic sector of the NW European margin (NE Atlantic) to investigate the paleoceanographic and paleoclimatic evolution of the area over the last few climatic cycles. We focus on periods characteristic of deglacial transitions. We adopt a multiproxy(More)
Current search engines offer limited assistance for exploration and information discovery in complex search tasks. Instead, users are distracted by the need to focus their cognitive efforts on finding navigation cues, rather than selecting relevant information. Interactive intent modeling enhances the human information exploration capacity through(More)
We introduce <i>IntentRadar</i>, an interactive search user interface that anticipates user's search intents by estimating them from user interaction. The estimated intents are represented as keywords and visualized on a radial layout that organizes the keywords as directions in the information space. <i>IntentRadar</i> assists users to direct their search(More)
Searching for relevant documents from a vast amount of scientific data is a challenging problem that requires a close interaction between the user, the search interface and the search engine. This extended abstract summarizes recent research on Intent Radar, an interactive search user interface that allows the user to directly interact with her estimated(More)
Researchers must navigate big data. Current scientific knowledge includes 50 million published articles. How can a system help a researcher find relevant documents in her field? The key is to model the researcher’s information need, and use Bayesian optimization to interactively improve the model. We introduce IntentRadar, an interactive search user(More)
Researchers must navigate big data. Current scientific knowledge includes 50 million published articles. How can a system help a researcher find relevant documents in her field? We introduce IntentRadar, an interactive search user interface and search engine that anticipates user’s search intents by estimating them form user’s interaction with the(More)
Following recent advances in visual interfaces for search, we investigate how to visualize activity traces to support collaborative information-seeking. We implemented a prototype system that visualizes traces of three types of activities (queries typed, articles bookmarked, and interested keywords) on top of a recent visual search system. The current(More)
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