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

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    GT-CSE-2013-07.pdf (1.045Mb)
    Date
    2013
    Author
    Choo, Jaegul
    Lee, Changhyun
    Clarkson, Edward
    Liu, Zhicheng
    Lee, Hanseung
    Chau, Duen Horng (Polo)
    Li, Fuxin
    Kannan, Ramakrishnan
    Stolper, Charles D.
    Inouye, David
    Mehta, Nishant
    Ouyang, Hua
    Som, Subhojit
    Gray, Alexander
    Stasko, John
    Park, Haesun
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    Abstract
    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.
    URI
    http://hdl.handle.net/1853/49251
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    • College of Computing Technical Reports [506]
    • School of Computational Science and Engineering Technical Reports [37]

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