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 › peer-review

12 Scopus citations

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
State	Published - Feb 13 2015
Externally published	Yes
Event	2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Paris, France Duration: Oct 9 2014 → Oct 14 2014

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	10/9/14 → 10/14/14

Funding

Funders	Funder number
National Stroke Foundation	CCF-0808863

Keywords

Recommendation
clustering
dimension reduction
document analysis
information retrieval
scatter plot

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). Article 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

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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title = "VisIRR: Visual analytics for information retrieval and recommendation with large-scale document data",

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 = "Recommendation, clustering, dimension reduction, document analysis, information retrieval, scatter plot",

author = "Jaegul Choo and Changhyun Lee and Hannah Kim and Hanseung Lee and Zhicheng Liu and Ramakrishnan Kannan and Stolper, {Charles D.} and John Stasko and Drake, {Barry L.} and Haesun Park",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 ; Conference date: 09-10-2014 Through 14-10-2014",

year = "2015",

month = feb,

day = "13",

doi = "10.1109/VAST.2014.7042511",

language = "English",

series = "2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "243--244",

editor = "Min Chen and David Ebert and Chris North",

booktitle = "2014 IEEE Conference on Visual Analytics Science and Technology, VAST 2014 - Proceedings",

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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, 10/9/14. 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 › peer-review

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AU - Kannan, Ramakrishnan

AU - Stolper, Charles D.

AU - Stasko, John

AU - Drake, Barry L.

AU - Park, Haesun

PY - 2015/2/13

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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.

KW - Recommendation

KW - clustering

KW - dimension reduction

KW - document analysis

KW - information retrieval

KW - scatter plot

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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

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

Abstract

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Keywords

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