DocumentCode :
119527
Title :
VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data
Author :
Jaegul Choo ; Changhyun Lee ; Hannah Kim ; Hanseung Lee ; Zhicheng Liu ; Kannan, Ramakrishnan ; Stolper, Charles D. ; Stasko, John ; Drake, Barry L. ; Haesun Park
fYear :
2014
fDate :
25-31 Oct. 2014
Firstpage :
243
Lastpage :
244
Abstract :
We present VisIRR, an interactive visual information retrieval and recommendation system for large-scale document data. Starting with a query, VisIRR visualizes the retrieved documents in a scatter plot along with their topic summary. Next, based on interactive personalized preference feedback on the documents, VisIRR collects and visualizes potentially relevant documents out of the entire corpus so that an integrated analysis of both retrieved and recommended documents can be performed seamlessly.
Keywords :
data visualisation; document handling; interactive systems; query processing; recommender systems; VisIRR; interactive personalized preference feedback; interactive visual information retrieval and recommendation system; large-scale document data; query; recommended documents; retrieved documents visualization; topic summary; visual analytics; Alzheimer´s disease; Data visualization; Information retrieval; Support vector machines; Visual analytics; Recommendation; clustering; dimension reduction; document analysis; information retrieval; scatter plot;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Visual Analytics Science and Technology (VAST), 2014 IEEE Conference on
Conference_Location :
Paris
Type :
conf
DOI :
10.1109/VAST.2014.7042511
Filename :
7042511
Link To Document :
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