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Stasko, John T.

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End date: 2019 × Start date: 2010 × Item Type: Publication ×

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VisIRR: Interactive Visual Information Retrieval and Recommendation for Large-scale Document Data

2013 , Choo, Jaegul , Lee, Changhyun , Clarkson, Edward , Liu, Zhicheng , Lee, Hanseung , Chau, Duen Horng , Li, Fuxin , Kannan, Ramakrishnan , Stolper, Charles D. , Inouye, David , Mehta, Nishant , Ouyang, Hua , Som, Subhojit , Gray, Alexander , Stasko, John T. , Park, Haesun

We present a visual analytics system called VisIRR, which is an interactive visual information retrieval and recommendation system for document discovery. VisIRR effectively combines both paradigms of passive pull through a query processes for retrieval and active push that recommends the items of potential interest based on the user preferences. Equipped with efficient dynamic query interfaces for a large corpus of document data, VisIRR visualizes the retrieved documents in a scatter plot form with their overall topic clusters. At the same time, based on interactive personalized preference feedback on documents, VisIRR provides recommended documents reaching out to the entire corpus beyond the retrieved sets. Such recommended documents are represented in the same scatter space of the retrieved documents so that users can perform integrated analyses of both retrieved and recommended documents seamlessly. We describe the state-of-the-art computational methods that make these integrated and informative representations as well as real time interaction possible. We illustrate the way the system works by using detailed usage scenarios. In addition, we present a preliminary user study that evaluates the effectiveness of the system.

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EasyZoom: Zoom-in-Context Views for Exploring Large Collections of Images

2013 , Chen, Jiajian , Xu, Yan , Turk, Greg , Stasko, John T.

Image browsing and searching are some of the most common tasks in daily computer use. Zooming techniques are important for image searching and browsing in a large collection of thumbnail images in a single screen. In this paper we investigate the design and usability of different zoom-in-context views for image browsing and searching. We present two new zoom-in-context views, sliding and expanding views, that can help users explore a large collection of images more efficiently and enjoyably. In the sliding view the zoomed image moves its neighbors away vertically and horizontally. In the expanding view, the nearby images are pushed away in all directions, and this method uses a Voronoi diagram to compute the positions of the neighbors. We also present the results of a user study that compared the usability of the two zoom-in-context views and an overlapping, non-context zoom in the tasks of searching to match an image or a text description, and the task of brochure making. Although the task completion times were not significantly different, users expressed a preference for the zoom-in-context methods over the standard non-context zoom for text-matching image search and for image browsing tasks.

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