VisIRR: Visual Analytics for Information Retrieval and Recommendation with Large-Scale Document Data

被引:0
作者
Choo, Jaegul [1 ]
Lee, Changhyun [2 ]
Kim, Hannah [1 ]
Lee, Hanseung [3 ]
Liu, Zhicheng [4 ]
Kannan, Ramakrishnan [1 ]
Stolper, Charles D. [1 ]
Stasko, John [1 ]
Drake, Barry L. [5 ]
Park, Haesun [1 ]
机构
[1] Georgia Inst Technol, Atlanta, GA 30332 USA
[2] Google Inc, Menlo Pk, CA USA
[3] Univ Maryland, College Pk, MD 20742 USA
[4] Adobe Res, San Jose, CA USA
[5] Georgia Tech Res Inst, Atlanta, GA 30332 USA
来源
2014 IEEE CONFERENCE ON VISUAL ANALYTICS SCIENCE AND TECHNOLOGY (VAST) | 2014年
关键词
Recommendation; document analysis; dimension reduction; clustering; information retrieval; scatter plot;
D O I
暂无
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
收藏
页码:243 / 244
页数:2
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