research-article

Distribution sliders: visualizing data distributions in range selection sliders

Authors:

Jürgen ZieglerAuthors Info & Claims

MuC '20: Proceedings of Mensch und Computer 2020

Pages 67 - 78

https://doi.org/10.1145/3404983.3405512

Published: 06 September 2020 Publication History

Abstract

Sliders are often used to enable users to easily enter preferences for continuous data. Although efforts have already been made to enrich and improve these interaction tools with additional information and visualizations, only rather basic variants of sliders are commonly used in online shops or databases. However, these sliders often provide users only with very limited information about underlying data.

We describe and evaluate three different slider designs, which enrich the tools with information in various ways, enabling users to efficiently explore the space of available items and to choose items in an informed manner. In one of the described slider designs we propose a new approach that integrates item recommendations directly into the slider, enabling users to see suitable items within the selection tool. In two user studies we show that these enhancements, both visualizations and recommendations, are powerful methods to directly support users in their searches.

References

[1]

Christopher Ahlberg and Ben Shneiderman. 1994. The Alphaslider: A Compact and Rapid Selector. In Conference Companion on Human Factors in Computing Systems (CHI '94). Association for Computing Machinery, New York, NY, USA, 226.

[2]

Christopher Ahlberg and Ben Shneiderman. 2003. Visual Information Seeking: Tight Coupling of Dynamic Query Filters with Starfield Displays. In The Craft of Information Visualization. Morgan Kaufmann, San Francisco, 7--13.

[3]

James Ahrens, Sébastien Jourdain, Patrick O'Leary, John Patchett, David H. Rogers, and Mark Petersen. 2014. An Image-Based Approach to Extreme Scale in Situ Visualization and Analysis. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC '14). IEEE Press, 424--434.

Digital Library

[4]

Dan Ariely. 2001. Seeing Sets: Representation by Statistical Properties. Psychological Science 12, 2 (2001), 157--162.

[5]

RA Bailey. 1992. Efficient Semi-latin Squares. Statistica Sinica (1992), 413--437.

[6]

Yoav Benjamini and Yosef Hochberg. 1995. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological) 57, 1 (1995), 289--300.

[7]

Raj Chandra Bose. 1939. On the Construction of Balanced Incomplete Block Designs. Annals of Eugenics 9, 4 (1939), 353--399.

[8]

Svetlin Bostandjiev, John O'Donovan, and Tobias Höllerer. 2012. TasteWeights: A Visual Interactive Hybrid Recommender System. In Proceedings of the Sixth ACM Conference on Recommender Systems (RecSys '12). 35--42.

Digital Library

[9]

Andreas Buja, John Alan McDonald, John Michalak, and Werner Stuetzle. 1991. Interactive Data Visualization Using Focusing and Linking. In Proceedings of the 2nd Conference on Visualization (VIS '91). IEEE Computer Society Press, 156--163.

Digital Library

[10]

William S. Cleveland. 1985. The Elements of Graphing Data. Wadsworth Publ. Co., USA.

[11]

Jacob Cohen. 1992. A Power Primer. Psychological Bulletin 112, 1 (1992), 155.

[12]

David P Doane. 1976. Aesthetic Frequency Classifications. The American Statistician 30, 4 (1976), 181--183.

[13]

Miriam Greis, Hendrik Schuff, Marius Kleiner, Niels Henze, and Albrecht Schmidt. 2017. Input Controls for Entering Uncertain Data: Probability Distribution Sliders. Proc. ACM Hum.-Comput. Interact. 1, EICS, Article 3 (June 2017), 17 pages.

Digital Library

[14]

Marti Hearst. 2009. Search User Interfaces. Cambridge University Press.

[15]

Katja Herrmanny, Sandra Schering, Ralf Berger, Benedikt Loepp, Timo Günter, Tim Hussein, and Jürgen Ziegler. 2014. MyMovieMixer: Ein hybrider Recommender mit visuellem Bedienkonzept. Mensch & Computer 2014-Tagungsband (2014).

[16]

Jonathan Koren, Yi Zhang, and Xue Liu. 2008. Personalized Interactive Faceted Search. In Proceedings of the 17th International Conference on World Wide Web (WWW '08). Association for Computing Machinery, New York, NY, USA, 477--486.

Digital Library

[17]

Tom Lanning, Kent Wittenburg, Michael Heinrichs, Christina Fyock, and Glenn Li. 2000. Multidimensional Information Visualization through Sliding Rods. In Proceedings of the Working Conference on Advanced Visual Interfaces (AVI '00). Association for Computing Machinery, New York, NY, USA, 173--180.

Digital Library

[18]

Adam M Larson, Tyler E Freeman, Ryan V Ringer, and Lester C Loschky. 2014. The Spatiotemporal Dynamics of Scene Gist Recognition. Journal of Experimental Psychology: Human Perception and Performance 40, 2 (2014), 471.

[19]

Yann Laurillau, Van-Bao Nguyen, Joëlle Coutaz, Gaëlle Calvary, Nadine Mandran, Fatoumata Camara, and Raffaella Balzarini. 2018. The TOP-Slider for Multi-Criteria Decision Making by Non-Specialists. In Proceedings of the 10th Nordic Conference on Human-Computer Interaction (NordiCHI '18). Association for Computing Machinery, New York, NY, USA, 642--653.

Digital Library

[20]

Qing Li, Xiaofeng Bao, Chen Song, Jinfei Zhang, and Chris North. 2003. Dynamic Query Sliders vs. Brushing Histograms. In Extended Abstracts on Human Factors in Computing Systems (CHI EA '03). Association for Computing Machinery, New York, NY, USA, 834--835.

[21]

Rensis Likert. 1932. A Technique for the Measurement of Attitudes. Archives of Psychology (1932).

[22]

Justin Matejka, Michael Glueck, Tovi Grossman, and George Fitzmaurice. 2016. The Effect of Visual Appearance on the Performance of Continuous Sliders and Visual Analogue Scales. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '16). Association for Computing Machinery, New York, NY, USA, 5421--5432.

Digital Library

[23]

Ulric Neisser. 2019. The Control of Information Pickup in Selective Looking. In Perception and its Development. Psychology Press, 201--219.

[24]

Kristin Potter, Mike Kirby, Dongbin Xiu, and Chris R Johnson. 2012. Interactive Visualization of Probability and Cumulative Density Functions. International Journal for Uncertainty Quantification 2, 4 (2012).

[25]

Pearl Pu, Li Chen, and Rong Hu. 2012. Evaluating Recommender Systems from the User's Perspective: Survey of the State of the Art. User Modeling and User-Adapted Interaction 22, 4--5 (2012), 317--355.

[26]

Francesco Ricci, Lior Rokach, and Bracha Shapira. 2011. Introduction to Recommender Systems Handbook. In Recommender Systems Handbook. Springer, 1--35.

[27]

Samuel Sanford Shapiro and Martin B Wilk. 1965. An Analysis of Variance Test for Normality (complete samples). Biometrika 52, 3/4 (1965), 591--611.

[28]

Ben Shneiderman. 1997. Direct Manipulation for Comprehensible, Predictable and Controllable User Interfaces. In Proceedings of the 2nd International Conference on Intelligent User Interfaces (IUI '97). 33--39.

Digital Library

[29]

Steven A Sloman. 1996. The Empirical Case for Two Systems of Reasoning. Psychological Bulletin 119, 1 (1996), 3.

[30]

E. Isaac Sparling and Shilad Sen. 2011. Rating: How Difficult is It?. In Proceedings of the Fifth ACM Conference on Recommender Systems (RecSys '11). Association for Computing Machinery, New York, NY, USA, 149--156.

Digital Library

[31]

Herbert A. Sturges. 1926. The Choice of a Class Interval. J. Amer. Statist. Assoc. 21, 153 (1926), 65--66.

[32]

Danielle Albers Szafir. 2018. The Good, the Bad, and the Biased: Five Ways Visualizations Can Mislead (and How to Fix Them). Interactions 25, 4 (June 2018), 26--33.

Digital Library

[33]

Danielle Albers Szafir, Steve Haroz, Michael Gleicher, and Steven Franconeri. 2016. Four Types of Ensemble Coding in Data Visualizations. Journal of Vision 16, 5 (2016), 11.

[34]

Nava Tintarev and Judith Masthoff. 2012. Evaluating the Effectiveness of Explanations for Recommender Systems. User Modeling and User-Adapted Interaction 22, 4--5 (2012), 399--439.

Digital Library

[35]

Lisa Tweedie, Robert Spence, Huw Dawkes, and Hus Su. 1996. Externalising Abstract Mathematical Models. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '96). Association for Computing Machinery, New York, NY, USA, 406--ff.

Digital Library

[36]

Kodzo Wegba, Aidong Lu, Yuemeng Li, and Wencheng Wang. 2018. Interactive Storytelling for Movie Recommendation through Latent Semantic Analysis. In 23rd International Conference on Intelligent User Interfaces (IUI '18). Association for Computing Machinery, New York, NY, USA, 521--533.

Digital Library

[37]

Rand R Wilcox. 2011. Introduction to Robust Estimation and Hypothesis Testing. Academic Press.

[38]

Ka-Ping Yee, Kirsten Swearingen, Kevin Li, and Marti Hearst. 2003. Faceted Metadata for Image Search and Browsing. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '03). Association for Computing Machinery, New York, NY, USA, 401--408.

Digital Library

Cited By

Suchmann LKrämer NZiegler JBoratto LGena CMarras MGermanakos PPopescus E(2024)Dynamic Ridge Plot Sliders: Supporting Users' Understanding of the Item Space Structure and Feature Dependencies in Interactive Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664872(106-113)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664872
Kleemann TLoepp BZiegler J(2024)Knowledge Graph-Based Integration of Conversational Advisors and Faceted FilteringInteracting with Computers10.1093/iwc/iwae044Online publication date: 18-Sep-2024
https://doi.org/10.1093/iwc/iwae044
Anjaria K(2023)Z-Delphi: A Z-Number-Based Delphi Technique for Technological Forecasting to Reduce Optimism/Pessimism Bias in Experts’ Convergent OpinionsInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00270-116:1Online publication date: 30-May-2023
https://doi.org/10.1007/s44196-023-00270-1
Show More Cited By

Index Terms

Distribution sliders: visualizing data distributions in range selection sliders
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interactive systems and tools
  2. Visualization
    1. Empirical studies in visualization
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
    2. Users and interactive retrieval
      1. Search interfaces

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cover image ACM Other conferences

MuC '20: Proceedings of Mensch und Computer 2020

September 2020

523 pages

ISBN:9781450375405

DOI:10.1145/3404983

General Chairs:
Bernhard Preim
Universität Magdeburg
,
Andreas Nürnberger
Universität Magdeburg
,
Christian Hansen
Universität Magdeburg

Copyright © 2020 ACM.

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Publication History

Published: 06 September 2020

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MuC'20

MuC'20: Mensch und Computer 2020

September 6 - 9, 2020

Magdeburg, Germany

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Cited By

Suchmann LKrämer NZiegler JBoratto LGena CMarras MGermanakos PPopescus E(2024)Dynamic Ridge Plot Sliders: Supporting Users' Understanding of the Item Space Structure and Feature Dependencies in Interactive Recommender SystemsAdjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization10.1145/3631700.3664872(106-113)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3631700.3664872
Kleemann TLoepp BZiegler J(2024)Knowledge Graph-Based Integration of Conversational Advisors and Faceted FilteringInteracting with Computers10.1093/iwc/iwae044Online publication date: 18-Sep-2024
https://doi.org/10.1093/iwc/iwae044
Anjaria K(2023)Z-Delphi: A Z-Number-Based Delphi Technique for Technological Forecasting to Reduce Optimism/Pessimism Bias in Experts’ Convergent OpinionsInternational Journal of Computational Intelligence Systems10.1007/s44196-023-00270-116:1Online publication date: 30-May-2023
https://doi.org/10.1007/s44196-023-00270-1
Höhn MWunderlich MBallweg KKohlhammer JLandesberger T(2023)Interactive Input and Visualization for Planning with Temporal UncertaintySN Computer Science10.1007/s42979-023-01705-44:3Online publication date: 23-Feb-2023
https://doi.org/10.1007/s42979-023-01705-4
Kleemann TZiegler J(2023)Blending Conversational Product Advisors and Faceted Filtering in a Graph-Based ApproachHuman-Computer Interaction – INTERACT 202310.1007/978-3-031-42286-7_8(137-159)Online publication date: 28-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-42286-7_8

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