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

Choo, Jaegul; Kim, Hannah; Clarkson, Edward; Liu, Zhicheng; Lee, Changhyun; Li, Fuxin; Lee, Hanseung; Kannan, Ramakrishnan; Stolper, Charles D.; Stasko, John; Park, Haesun

doi:10.1145/3070616

Title: VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data

Journal Article · Tue Jan 30 23:00:00 EST 2018 · ACM Transactions on Knowledge Discovery from Data

DOI: https://doi.org/10.1145/3070616 · OSTI ID:1426558

Choo, Jaegul ^[1]; Kim, Hannah ^[2]; Clarkson, Edward ^[2]; Liu, Zhicheng ^[3]; Lee, Changhyun ^[4]; Li, Fuxin ^[5]; Lee, Hanseung ^[4];

^[6]; Stolper, Charles D. ^[7]; Stasko, John ^[2]; Park, Haesun ^[2]

Korea University, Seoul (South Korea)
Georgia Inst. of Technology, Atlanta, GA (United States)
Adobe Research, Seattle, WA (United States)
Google Inc., Mountain View, CA (United States)
Oregon State University, Corvallis, OR (United States)
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Southwestern University, Georgetown, TX (United States)

In this paper, we present an interactive visual information retrieval and recommendation system, called VisIRR, for large-scale document discovery. VisIRR effectively combines the paradigms of (1) a passive pull through query processes for retrieval and (2) an active push that recommends items of potential interest to users based on their preferences. Equipped with an efficient dynamic query interface against a large-scale corpus, VisIRR organizes the retrieved documents into high-level topics and visualizes them in a 2D space, representing the relationships among the topics along with their keyword summary. In addition, based on interactive personalized preference feedback with regard to documents, VisIRR provides document recommendations from the entire corpus, which are beyond the retrieved sets. Such recommended documents are visualized in the same space as the retrieved documents, so that users can seamlessly analyze both existing and newly recommended ones. This article presents novel computational methods, which make these integrated representations and fast interactions possible for a large-scale document corpus. We illustrate how the system works by providing detailed usage scenarios. Finally, we present preliminary user study results for evaluating the effectiveness of the system.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1426558

Journal Information:: ACM Transactions on Knowledge Discovery from Data, Journal Name: ACM Transactions on Knowledge Discovery from Data Journal Issue: 1 Vol. 12; ISSN 1556-4681

Publisher:: Association for Computing MachineryCopyright Statement

Country of Publication:: United States

Language:: English

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