VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choo, Jaegul | - |
dc.contributor.author | Kim, Hannah | - |
dc.contributor.author | Clarkson, Edward | - |
dc.contributor.author | Liu, Zhicheng | - |
dc.contributor.author | Lee, Changhyun | - |
dc.contributor.author | Li, Fuxin | - |
dc.contributor.author | Lee, Hanseung | - |
dc.contributor.author | Kannan, Ramakrishnan | - |
dc.contributor.author | Stolper, Charles D. | - |
dc.contributor.author | Stasko, John | - |
dc.contributor.author | Park, Haesun | - |
dc.date.accessioned | 2021-09-02T15:14:58Z | - |
dc.date.available | 2021-09-02T15:14:58Z | - |
dc.date.created | 2021-06-16 | - |
dc.date.issued | 2018-02 | - |
dc.identifier.issn | 1556-4681 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/77480 | - |
dc.description.abstract | In this article, 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. Additionally, we present preliminary user study results for evaluating the effectiveness of the system. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.title | VisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choo, Jaegul | - |
dc.identifier.doi | 10.1145/3070616 | - |
dc.identifier.scopusid | 2-s2.0-85042531976 | - |
dc.identifier.wosid | 000426898500008 | - |
dc.identifier.bibliographicCitation | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, v.12, no.1 | - |
dc.relation.isPartOf | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA | - |
dc.citation.title | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA | - |
dc.citation.volume | 12 | - |
dc.citation.number | 1 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordAuthor | Recommendation | - |
dc.subject.keywordAuthor | information retrieval | - |
dc.subject.keywordAuthor | dimension reduction | - |
dc.subject.keywordAuthor | topic modeling | - |
dc.subject.keywordAuthor | clustering | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
(02841) 서울특별시 성북구 안암로 14502-3290-1114
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.