User Similarity and Deviation Analysis for Adaptive Visualizations

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8521)


Adaptive visualizations support users in information acquisition and exploration and therewith in human access of data. Their adaptation effect is often based on approaches that require the training by an expert. Further the effects often aims to support just the individual aptitudes. This paper introduces an approach for modeling a canonical user that makes the predefined training-files dispensable and enables an adaptation of visualizations for the majority of users. With the introduced user deviation algorithm, the behavior of individuals can be compared to the average user behavior represented in the canonical user model to identify behavioral anomalies. The further introduced similarity measurements allow to cluster similar deviated behavioral patterns as groups and provide them effective visual adaptations.


User Group Data Element User Model Individual User Deviation Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Golemati, M., Halatsis, C., Vassilakis, C., Katifori, A., Lepouras, G.: A context-based adaptive visualization environment. In: Proceedings of the Conference on Information Visualization, IV 2006, pp. 62–67. IEEE Computer Society, Washington, DC (2006)Google Scholar
  2. 2.
    Golemati, M., Vassilakis, C., Katifori, A., Lepouras, G., Halatsis, C.: Context and adaptivity-driven visualization method selection. In: Mourlas, C., Germanakos, P. (eds.) Intelligent User Interfaces: Adaptation and Personalization Systems and Technologies, pp. 188–204. IGI Global (2009)Google Scholar
  3. 3.
    Gotz, D., When, Z., Lu, J., Kissa, P., Cao, N., Qian, W.H., Liu, S.X., Zhou, M.X.: Harvest: An intelligent visual analytic tool for the masses. In: Proceedings of the First International Workshop on Intelligent Visual Interfaces for Text Analysis, IVITA 2010, pp. 1–4. ACM, New York (2010)CrossRefGoogle Scholar
  4. 4.
    Mackinlay, J.: Automating the design of graphical presentations of relational information. ACM Trans. Graph. 5, 110–141 (1986)CrossRefGoogle Scholar
  5. 5.
    Mackinlay, J., Hanrahan, P., Stolte, C.: Show me: Automatic presentation for visual analysis. IEEE Transactions on Visualization and Computer Graphics 13, 1137–1144 (2007)CrossRefGoogle Scholar
  6. 6.
    Shi, L., Cao, N., Liu, S., Qian, W., Tan, L., Wang, G., Sun, J., Lin, C.Y.: Himap: Adaptive visualization of large-scale online social networks. In: Visualization Symposium, PacificVis 2009, pp. 41–48. IEEE Pacific (2009)Google Scholar
  7. 7.
    da Silva, I., Santucci, G., del Sasso Freitas, C.: Ontology Visualization: One Size Does Not Fit All. In: EuroVA 2012: International Workshop on Visual Analytics, pp. 91–95. Eurographics Association (2012)Google Scholar
  8. 8.
    Ahn, J.W., Brusilovsky, P.: Adaptive visualization of search results: Bringing user models to visual analytics. Information Visualization 8, 180–196 (2009)CrossRefGoogle Scholar
  9. 9.
    Ahn, J.W.: Adaptive Visualization for Focused Personalized Information Retrieval. PhD thesis, School of Information Sciences, University of Pittsburgh (2010)Google Scholar
  10. 10.
    Nazemi, K., Stab, C., Fellner, D.W.: Interaction analysis for adaptive user interfaces. In: Huang, D.-S., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2010. LNCS, vol. 6215, pp. 362–371. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  11. 11.
    Nazemi, K., Stab, C., Fellner, D.W.: Interaction analysis: An algorithm for interaction prediction and activity recognition in adaptive systems. In: Proc. of IEEE ICIS, pp. 607–612. IEEE Press, New York (2010)Google Scholar
  12. 12.
    Nazemi, K., Stab, C., Kuijper, A.: A reference model for adaptive visualization systems. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part I, HCII 2011. LNCS, vol. 6761, pp. 480–489. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    Anderson, C.R., Domingos, P., Weld, D.S.: Relational markov models and their application to adaptive web navigation. In: KDD 2002: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 143–152. ACM, New York (2002)Google Scholar
  14. 14.
    Nazemi, K., Retz, R., Bernard, J., Kohlhammer, J., Fellner, D.: Adaptive semantic visualization for bibliographic entries. In: Bebis, G., et al. (eds.) ISVC 2013, Part II. LNCS, vol. 8034, pp. 13–24. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  15. 15.
    Nazemi, K., Breyer, M., Forster, J., Burkhardt, D., Kuijper, A.: Interacting with semantics: A user-centered visualization adaptation based on semantics data. In: Smith, M.J., Salvendy, G. (eds.) Human Interface, HCII 2011, Part I. LNCS, vol. 6771, pp. 239–248. Springer, Heidelberg (2011)Google Scholar
  16. 16.
    Sleeman, D.: Umfe: a user modelling front-end subsystem. Int. J. Man-Mach. Stud., 71–88 (1985)Google Scholar
  17. 17.
    Brusilovsky, P., Millán, E.: User models for adaptive hypermedia and adaptive educational systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 3–53. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Nazemi, K., Burkhardt, D., Breyer, M., Kuijper, A.: Modeling users for adaptive semantics visualizations. In: Stephanidis, C. (ed.) Universal Access in HCI, Part II, HCII 2011. LNCS, vol. 6766, pp. 88–97. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Luo, H., Niu, C., Shen, R., Ullrich, C.: A collaborative filtering framework based on both local user similarity and global user similarity. Mach. Learn., 231–245 (2008)Google Scholar
  20. 20.
    Gong, S.: A collaborative filtering recommendation algorithm based on user clustering and item clustering. Journal of Software 5, 745–752 (2010)Google Scholar
  21. 21.
    Guo, L., Peng, Q.: A combinative similarity computing measure for collaborative filtering. In: Proceedings of ICCSEE 2013. Advances in Intelligent Systems Research, pp. 1921–1924. Atlantis Press (2013)Google Scholar
  22. 22.
    Brusilovsky, P., wook Ahn, J., Dumitriu, T., Yudelson, M.: Adaptive knowledge-based visualization for accessing educational examples. In: Tenth International Conference on Information Visualization, IV 2006, pp. 142–150 (2006)Google Scholar
  23. 23.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Feature-weighted user model for recommender systems. In: Conati, C., McCoy, K., Paliouras, G. (eds.) UM 2007. LNCS (LNAI), vol. 4511, pp. 97–106. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Fraunhofer IGDDarmstadtGermany
  2. 2.Technische Universität DarmstadtDarmstadtGermany

Personalised recommendations