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dc.contributor.authorChoo, Jaegul
dc.contributor.authorLee, Changhyun
dc.contributor.authorClarkson, Edward
dc.contributor.authorLiu, Zhicheng
dc.contributor.authorLee, Hanseung
dc.contributor.authorChau, Duen Horng (Polo)
dc.contributor.authorLi, Fuxin
dc.contributor.authorKannan, Ramakrishnan
dc.contributor.authorStolper, Charles D.
dc.contributor.authorInouye, David
dc.contributor.authorMehta, Nishant
dc.contributor.authorOuyang, Hua
dc.contributor.authorSom, Subhojit
dc.contributor.authorGray, Alexander
dc.contributor.authorStasko, John
dc.contributor.authorPark, Haesun
dc.date.accessioned2013-10-23T21:25:29Z
dc.date.available2013-10-23T21:25:29Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/1853/49251
dc.descriptionResearch areas: Machine learning, Data mining, Information visualization, Visual analytics, Text visualization.en_US
dc.description.abstractWe 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.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesCSE Technical Reports ; GT-CSE-13-07en_US
dc.subjectClusteringen_US
dc.subjectDimension reductionen_US
dc.subjectDocument analysisen_US
dc.subjectInformation retrievalen_US
dc.subjectRecommendationen_US
dc.subjectScatter ploten_US
dc.titleVisIRR: Interactive Visual Information Retrieval and Recommendation for Large-scale Document Dataen_US
dc.typeTechnical Reporten_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Computational Science and Engineeringen_US
dc.contributor.corporatenameGeorgia Tech Research Instituteen_US
dc.contributor.corporatenameStanford Universityen_US
dc.contributor.corporatenameUniversity of Marylanden_US
dc.contributor.corporatenameUniversity of Texas at Austinen_US
dc.embargo.termsnullen_US


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