Evaluating the Memorability and Readability of Micro-filter Visualisations

Gerwald Tschinkel, Vedran Sabol

Abstract

When using classical search engines, researchers are often confronted with a number of results far beyond what they can realistically manage to read; when this happens, recommender systems can help, by pointing users to the most valuable sources of information. In the course of a long-term research project, research into one area can extend over several days, weeks, or even months. Interruptions are unavoidable, and, when multiple team members have to discuss the status of a project, it’s important to be able to communicate the current research status easily and accurately. Multiple type-specific interactive views can help users identify the results most relevant to their focus of interest. Our recommendation dashboard uses micro-filter visualizations intended to improve the experience of working with multiple active filters, allowing researchers to maintain an overview of their progress. Within this paper, we carry out an evaluation of whether micro-visualizations help to increase the memorability and readability of active filters in comparison to textual filters. Five tasks, quantitative and qualitative questions, and the separate view on the different visualisation types enabled us to gain insights on how micro-visualisations behave and will be discussed throughout the paper.

References

  1. Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., and Pfister, H. (2013). What makes a visualization memorable? IEEE Transactions on Visualization and Computer Graphics, 19(12):2306-2315.
  2. Brady, T. F., Konkle, T., Alvarez, G. A., and Oliva, A. (2008). Visual long-term memory has a massive storage capacity for object details. Proceedings of the National Academy of Sciences, 105(38):14325- 14329.
  3. di Sciascio, C., Sabol, V., and Veas, E. E. (2015). urank: Exploring document recommendations through an interactive user-driven approach. In Proceedings of the Joint Workshop on Interfaces and Human Decision Making for Recommender Systems, IntRS, pages 29- 36.
  4. Edward, T. (2001). The visual display of quantitative information. Graphics Press, Cheshire, USA,, 4(5):6.
  5. English, J., Hearst, M., Sinha, R., Swearingen, K., and Lee, K. (2002). Flexible search and navigation using faceted metadata. Technical report, Technical report, University of Berkeley, School of Information Management and Systems, 2003. Submitted for publication.
  6. Hart, S. G. and Staveland, L. E. (1988). Development of nasa-tlx (task load index): Results of empirical and theoretical research. Advances in psychology, 52:139- 183.
  7. Hearst, M., Elliott, A., English, J., Sinha, R., Swearingen, K., and Yee, K.-P. (2002). Finding the flow in web site search. Communications of the ACM, 45(9):42-49.
  8. Kern, R., Jack, K., and Granitzer, M. (2014). Recommending scientific literature: Comparing use-cases and algorithms. arXiv preprint arXiv:1409.1357.
  9. Kienreich, W., Lex, E., and Seifert, C. (2008). Apa labs: an experimental web-based platform for the retrieval and analysis of news articles. In Applications of Digital Information and Web Technologies, 2008. ICADIWT 2008. First International Conference on the, pages 58-62. IEEE.
  10. Konkle, T., Brady, T. F., Alvarez, G. A., and Oliva, A. (2010). Conceptual distinctiveness supports detailed visual long-term memory for real-world objects. Journal of Experimental Psychology: General, 139(3):558.
  11. Lam, H., Bertini, E., Isenberg, P., Plaisant, C., and Carpendale, S. (2011). Seven guiding scenarios for information visualization evaluation.
  12. North, C. and Shneiderman, B. (1999). Snap-together visualization: Coordinating multiple views to explore information.
  13. Roberts, J. C. (2000). Multiple view and multiform visualization. In Electronic Imaging, pages 176-185. International Society for Optics and Photonics.
  14. Sabol, V. and Scharl, A. (2008). Visualizing temporalsemantic relations in dynamic information landscapes. In 11th International Conference on Geographic Information Science (AGILE-2008), Semantic Web Meets Geospatial Applications Workshop.
  15. Saket, B., Endert, A., and Stasko, J. (2016). Beyond usability and performance: A review of user experiencefocused evaluations in visualization. In Proceedings of the Beyond Time and Errors on Novel Evaluation Methods for Visualization, pages 133-142. ACM.
  16. Schlötterer, J., Seifert, C., and Granitzer, M. (2014). Webbased just-in-time retrieval for cultural content. In PATCH14: Proceedings of the 7th International ACM Workshop on Personalized Access to Cultural Heritage.
  17. Seifert, C., Bailer, W., Orgel, T., Gantner, L., Kern, R., Ziak, H., Petit, A., Schltterer, J., Zwicklbauer, S., and Granitzer, M. (to appear 2016). Ubiquitous access to digital cultural heritage. Journal on Computing and Cultural Heritage (JOCCH), page to appear.
  18. Seifert, C., Jurgovsky, J., and Granitzer, M. (2014). Facetscape: A visualization for exploring the search space. In 2014 18th International Conference on Information Visualisation, pages 94-101. IEEE.
  19. Tschinkel, G., Di Sciascio, C., Mutlu, B., and Sabol, V. (2015). The recommendation dashboard: A system to visualise and organise recommendations. In 2015 19th International Conference on Information Visualisation, pages 241-244. IEEE.
  20. Tschinkel, G., Hafner, R., Hasitschka, P., and Sabol, V. (2016). Using micro-visualisations to support faceted filtering of recommender results. In Information Visualisation (IV), 2016 20th International Conference, pages 318-323. IEEE.
Download


Paper Citation


in Harvard Style

Tschinkel G. and Sabol V. (2017). Evaluating the Memorability and Readability of Micro-filter Visualisations . In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017) ISBN 978-989-758-228-8, pages 186-197. DOI: 10.5220/0006272001860197


in Bibtex Style

@conference{ivapp17,
author={Gerwald Tschinkel and Vedran Sabol},
title={Evaluating the Memorability and Readability of Micro-filter Visualisations},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)},
year={2017},
pages={186-197},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006272001860197},
isbn={978-989-758-228-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: IVAPP, (VISIGRAPP 2017)
TI - Evaluating the Memorability and Readability of Micro-filter Visualisations
SN - 978-989-758-228-8
AU - Tschinkel G.
AU - Sabol V.
PY - 2017
SP - 186
EP - 197
DO - 10.5220/0006272001860197