VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data

Jaegul Choo; Changhyun Lee; Hannah Kim; Hanseung Lee; Zhicheng Liu; Ramakrishnan Kannan; Charles D. Stolper; John Stasko; Barry L. Drake; Haesun Park

doi:10.1109/VAST.2014.7042511

VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data

Jaegul Choo, Changhyun Lee, Hannah Kim, Hanseung Lee, Zhicheng Liu, Ramakrishnan Kannan, Charles D. Stolper, John Stasko, Barry L. Drake, Haesun Park

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

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.

Original language	English
Title of host publication	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings
Editors	Min Chen, David Ebert, Chris North
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	243-244
Number of pages	2
ISBN (Electronic)	9781479962273
DOIs	https://doi.org/10.1109/VAST.2014.7042511
Publication status	Published - 2015 Feb 13
Event	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Paris, France Duration: 2014 Oct 9 → 2014 Oct 14

Publication series

Name	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings

Conference

Conference	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014
Country/Territory	France
City	Paris
Period	14/10/9 → 14/10/14

Keywords

Recommendation
clustering
dimension reduction
document analysis
information retrieval
scatter plot

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Computer Science Applications

Access to Document

10.1109/VAST.2014.7042511

Cite this

Choo, J., Lee, C., Kim, H., Lee, H., Liu, Z., Kannan, R., Stolper, C. D., Stasko, J., Drake, B. L., & Park, H. (2015). VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data. In M. Chen, D. Ebert, & C. North (Eds.), 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings (pp. 243-244). [7042511] (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VAST.2014.7042511

VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data. / Choo, Jaegul; Lee, Changhyun; Kim, Hannah et al.
2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. ed. / Min Chen; David Ebert; Chris North. Institute of Electrical and Electronics Engineers Inc., 2015. p. 243-244 7042511 (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Choo, J, Lee, C, Kim, H, Lee, H, Liu, Z, Kannan, R, Stolper, CD, Stasko, J, Drake, BL & Park, H 2015, VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data. in M Chen, D Ebert & C North (eds), 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings., 7042511, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 243-244, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014, Paris, France, 14/10/9. https://doi.org/10.1109/VAST.2014.7042511

Choo J, Lee C, Kim H, Lee H, Liu Z, Kannan R et al. VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data. In Chen M, Ebert D, North C, editors, 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2015. p. 243-244. 7042511. (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings). doi: 10.1109/VAST.2014.7042511

Choo, Jaegul ; Lee, Changhyun ; Kim, Hannah et al. / VisIRR : Visual analytics for information retrieval and recommendation with large-scale document data. 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings. editor / Min Chen ; David Ebert ; Chris North. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 243-244 (2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings).

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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.",

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AB - 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.

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VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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