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

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dc.contributor.authorChoo, Jaegulko
dc.contributor.authorKim, Hannahko
dc.contributor.authorClarkson, Edwardko
dc.contributor.authorLiu, Zhichengko
dc.contributor.authorLee, Changhyunko
dc.contributor.authorLi, Fuxinko
dc.contributor.authorLee, Hanseungko
dc.contributor.authorKannan, Ramakrishnanko
dc.contributor.authorStolper, Charles D.ko
dc.contributor.authorStasko, Johnko
dc.contributor.authorPark, Haesunko
dc.date.accessioned2020-03-24T05:20:07Z-
dc.date.available2020-03-24T05:20:07Z-
dc.date.created2020-03-24-
dc.date.created2020-03-24-
dc.date.created2020-03-24-
dc.date.issued2018-02-
dc.identifier.citationACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA, v.12, no.1-
dc.identifier.issn1556-4681-
dc.identifier.urihttp://hdl.handle.net/10203/273418-
dc.description.abstractIn 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.languageEnglish-
dc.publisherASSOC COMPUTING MACHINERY-
dc.titleVisIRR: A Visual Analytics System for Information Retrieval and Recommendation for Large-Scale Document Data-
dc.typeArticle-
dc.identifier.wosid000426898500008-
dc.identifier.scopusid2-s2.0-85042531976-
dc.type.rimsART-
dc.citation.volume12-
dc.citation.issue1-
dc.citation.publicationnameACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA-
dc.identifier.doi10.1145/3070616-
dc.contributor.localauthorChoo, Jaegul-
dc.contributor.nonIdAuthorKim, Hannah-
dc.contributor.nonIdAuthorClarkson, Edward-
dc.contributor.nonIdAuthorLiu, Zhicheng-
dc.contributor.nonIdAuthorLee, Changhyun-
dc.contributor.nonIdAuthorLi, Fuxin-
dc.contributor.nonIdAuthorLee, Hanseung-
dc.contributor.nonIdAuthorKannan, Ramakrishnan-
dc.contributor.nonIdAuthorStolper, Charles D.-
dc.contributor.nonIdAuthorStasko, John-
dc.contributor.nonIdAuthorPark, Haesun-
dc.description.isOpenAccessN-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorRecommendation-
dc.subject.keywordAuthorinformation retrieval-
dc.subject.keywordAuthordimension reduction-
dc.subject.keywordAuthortopic modeling-
dc.subject.keywordAuthorclustering-
dc.subject.keywordPlusNONNEGATIVE MATRIX FACTORIZATION-
dc.subject.keywordPlusVISUALIZATIONS-
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