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VizRec: A Two-Stage Recommender System for Personalized Visualizations

Published: 29 March 2015 Publication History

Abstract

Identifying and using the information from distributed and heterogeneous information sources is a challenging task in many application fields. Even with services that offer well-defined structured content, such as digital libraries, it becomes increasingly difficult for a user to find the desired information. To cope with an overloaded information space, we propose a novel approach - VizRec - combining recommender systems (RS) and visualizations. VizRec suggests personalized visual representations for recommended data. One important aspect of our contribution and a prerequisite for VizRec are user preferences that build a personalization model. We present a crowd based evaluation and show how such a model of preferences can be elicited.

References

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Zheng, X. S., Lin, J. j. W., Zapf, S., and Knapheide, C. Visualizing user experience through "perceptual maps": Concurrent assessment of perceived usability and subjective appearance in car infotainment systems. In Proc. of ICDHM'07, Springer-Verlag (Berlin, Heidelberg, 2007), 536--545.

Cited By

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  • (2019)Chemical Cross-Linking Controls in Vitro Fecal Fermentation Rate of High-Amylose Maize Starches and Regulates Gut Microbiota CompositionJournal of Agricultural and Food Chemistry10.1021/acs.jafc.9b0441067:49(13728-13736)Online publication date: 16-Oct-2019
  • (2018)REMatch: Research Expert Matching System2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)10.1109/BDVA.2018.8534021(1-10)Online publication date: Oct-2018
  • (2017)Augmenting research cooperation in production engineering with data analyticsProduction Engineering10.1007/s11740-017-0715-x11:2(213-220)Online publication date: 10-Feb-2017
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  1. VizRec: A Two-Stage Recommender System for Personalized Visualizations

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    cover image ACM Conferences
    IUI '15 Companion: Companion Proceedings of the 20th International Conference on Intelligent User Interfaces
    March 2015
    164 pages
    ISBN:9781450333085
    DOI:10.1145/2732158
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 29 March 2015

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    Author Tags

    1. collaborative filtering
    2. crowd-based experiment
    3. recommender systems
    4. visualization recommendation

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    Funding Sources

    • EC 7th Framework project EEXCESS
    • Know-center GmbH

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    IUI'15
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    IUI '15 Companion Paper Acceptance Rate 47 of 205 submissions, 23%;
    Overall Acceptance Rate 746 of 2,811 submissions, 27%

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    IUI '25

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    Cited By

    View all
    • (2019)Chemical Cross-Linking Controls in Vitro Fecal Fermentation Rate of High-Amylose Maize Starches and Regulates Gut Microbiota CompositionJournal of Agricultural and Food Chemistry10.1021/acs.jafc.9b0441067:49(13728-13736)Online publication date: 16-Oct-2019
    • (2018)REMatch: Research Expert Matching System2018 International Symposium on Big Data Visual and Immersive Analytics (BDVA)10.1109/BDVA.2018.8534021(1-10)Online publication date: Oct-2018
    • (2017)Augmenting research cooperation in production engineering with data analyticsProduction Engineering10.1007/s11740-017-0715-x11:2(213-220)Online publication date: 10-Feb-2017
    • (2017)Usability of Visual Data Profiling in Data Cleaning and TransformationOn the Move to Meaningful Internet Systems. OTM 2017 Conferences10.1007/978-3-319-69459-7_32(480-496)Online publication date: 21-Oct-2017
    • (2016)Effects of Individual Differences in Working Memory on Plan Presentational ChoicesFrontiers in Psychology10.3389/fpsyg.2016.017937Online publication date: 16-Nov-2016
    • (2016)Context aware recommendation for data visualizationProceedings of the 2nd International Conference on Communication and Information Processing10.1145/3018009.3018027(22-26)Online publication date: 26-Nov-2016
    • (2016)BBookXProceedings of the 25th International Conference Companion on World Wide Web10.1145/2872518.2891077(929-933)Online publication date: 11-Apr-2016

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