User Modeling and User-Adapted Interaction

, Volume 17, Issue 3, pp 305–337 | Cite as

The role of human factors in stereotyping behavior and perception of digital library users: a robust clustering approach

  • Enrique Frias-Martinez
  • Sherry Y. ChenEmail author
  • Robert D. Macredie
  • Xiaohui Liu
Original Paper


To deliver effective personalization for digital library users, it is necessary to identify which human factors are most relevant in determining the behavior and perception of these users. This paper examines three key human factors: cognitive styles, levels of expertise and gender differences, and utilizes three individual clustering techniques: k-means, hierarchical clustering and fuzzy clustering to understand user behavior and perception. Moreover, robust clustering, capable of correcting the bias of individual clustering techniques, is used to obtain a deeper understanding. The robust clustering approach produced results that highlighted the relevance of cognitive style for user behavior, i.e., cognitive style dominates and justifies each of the robust clusters created. We also found that perception was mainly determined by the level of expertise of a user. We conclude that robust clustering is an effective technique to analyze user behavior and perception.


Digital libraries Human factors Stereotypes Robust clustering Perception Behavior 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Altman D.G. (1997). Practical Statistics for medical Research. Chapman and Hall, London Google Scholar
  2. Anastasi A. (1988). Psychological Testing. Macmillan, New York Google Scholar
  3. Bezdek J.C. (1981). Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York zbMATHGoogle Scholar
  4. Bryan-Kinns, N., Blandford, A., Thimbleby, H.: Interaction modelling for digital libraries. In: Workshop on Evaluation of Information Management Systems (2000)Google Scholar
  5. Callan, J., Smeaton, A., Beaulieu, M., Borlund, P., Brusilovsky, P., Chalmers, M., Lynch, C., Riedl, J., Smyth, B., Straccia, U., Toms E.: Personalization and recommender systems in Digital Libraries, Joint NSF-EU DELOS Working Group Report, URL Delos-NSF/Personalisation.pdf. (2003)Google Scholar
  6. Chen S.Y. (2002). A cognitive model for non-linear learning in hypermedia programmes. Br. J. Educ. Technol. 33: 453–464 Google Scholar
  7. Chen S.Y., Macredie R. (2002). Cognitive styles and hypermedia navigation: development of a earning model. J. Am. Soc. Inform. Sci. Technol. 53: 3–15 CrossRefGoogle Scholar
  8. Chen S.Y., Macredie R.D. (2004). Cognitive modelling of student learning in web-based instructional programmes. Int. J. Human-Comput. Interact. 17: 375–402 CrossRefGoogle Scholar
  9. Chin, J.P., Diehl, V.A., Normal, K.L.: Development of an instrument measuring user satisfaction of the human-computer interface. ACM CHI’88 Proceedings, pp. 213–218 (1988)Google Scholar
  10. Chiu S.L. (1994). Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2: 267–278 MathSciNetGoogle Scholar
  11. Cho Y.H., Kim J.K., Kim S.H. (2002). A personalized recommender system based on web usage mining and decision tree. Expert Syst. Appl. 23: 329–342 CrossRefGoogle Scholar
  12. Doux, A., Laurent, J., Nadal, J.: Symbolic data analysis with the k-means algorithm for user profiling, user modeling. In: Proceedings of the Sixth International Conference, UM97, pp. 359–361 (1997)Google Scholar
  13. Downing R.E., Moore J., Brown S. (2005). The effects and interaction of spatial visualization and domain expertise on information seeking. Comput. Human Behavi. 21: 195–209 CrossRefGoogle Scholar
  14. Everitt, B.S.: Cluster Analysis, 3rd ed. Arnold (1993)Google Scholar
  15. Ford N. (2000). Cognitive styles and virtual environments. J. Am. Soc. Inform. Sci. 51: 543–557 CrossRefGoogle Scholar
  16. Ford, N., Miller, D.: Gender differences in Internet perception and use. In: Electronic Library and Visual Information Research, Proceedings of the Third ELVIRA Conference, pp. 87–202 (1996)Google Scholar
  17. Ford N., Miller D., Moss N. (2005). Web search strategies and human individual differences. Cognitive and demographic factors, internet attitudes and approaches. J. Am. Soc. Inform. Sci. Technol. 56: 741–756 CrossRefGoogle Scholar
  18. Frias-Martinez E., Chen S., Liu X. (2007). Automatic cognitive style identification of digital library users for personalization. J. Am. Soc. Inform. Sci. Technol. 58(2): 237–251 CrossRefGoogle Scholar
  19. Fu, Y., Sandhu, K., Shih, M.Y.: Clustering of web users based on access patterns. In: Proceedings of the 1999 KDD Workshop on Web Mining, July 2002Google Scholar
  20. Goren-Bar D., Graziola I., Pianesi F., Zancanaro M. (2006). The influence of personality factors on visitor attitudes towards adaptivity dimensions for mobile museum guides. User Model. User-Adapted Interact. 16: 31–62 CrossRefGoogle Scholar
  21. Hay, B., Wets, G., Vanhoof, K.: Clustering navigation patterns on a website using a Sequence Alignment Method. In: IJCAI 2001 Workshop on Intelligent Techniques for Web Personalization (2001)Google Scholar
  22. Hong J., Heer J., Waterson S., Landay J.A. (2001). WebQuilt: a Proxy-based Approach to Remote Web Usability Testing. ACM Trans. Inform. Syst. 19: 263–385 CrossRefGoogle Scholar
  23. Ivory M.Y., Megraw R. (2005). Evolution of web site design patterns. ACM Trans. Inform. Syst. 23: 463–497 CrossRefGoogle Scholar
  24. Jain A., Dubes R.C. (1999). Data clustering. ACM Comput. Surv. 31: 264–323 CrossRefGoogle Scholar
  25. Joshi, A.K., Krishnapuram, R.: On mining Web Access Logs. In: Proceedings of the ACM-SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, pp. 63–69 (2000)Google Scholar
  26. Kobsa A. (2001). Generic user modeling systems. User Model. User-Adapted Interact. 11: 49–63 zbMATHCrossRefGoogle Scholar
  27. Krishnapuram R., Joshi A., Nasraoui O., Yi L. (2001). Low-complexity fuzzy relational clustering algorithms for web mining. IEEE Trans. Fuzzy Syst. 9: 595–608 CrossRefGoogle Scholar
  28. Lampinen, T., Koivisto, H.: Profiling network applications with fuzzy C-means clustering and self-organising map. In: International Conference on Fuzzy Systems and Knowledge Discovery, November 2002Google Scholar
  29. Large A., Beheshti J., Rahman T. (2002). Design criteria for children’s web portals: the users speak out. J. Am. Soc. Inform. Sci. Technol. 53: 79–94 CrossRefGoogle Scholar
  30. Lazander A.W., Biemans H.J., Wopereis I.G. (2000). Differences between novice and experienced users in searching information on the World Wide Web. J. Am. Soc. Inform. Sci. 51: 76–581 Google Scholar
  31. Lewis J.R. (1995). IBM computer usability satisfaction questionnaires: psychometric evaluation and instructions for user. Int. J. Human-Comput. Interact. 7: 57–78 CrossRefGoogle Scholar
  32. Ling J., Van Schaik P. (2006). The influence of font type and line length on visual search and information retrieval in web pages. Int. J. Human-Comput. Stud. 64: 395–404 CrossRefGoogle Scholar
  33. Liu H., Tarima S., Borders A.S., Getchell T.V., Getchell M.L., Stromberg A.J. (2005). Quadratic regression for gene discovery and pattern recognition for non cyclic short time-course microarray experiments. BMC Bioinform. 6: 106 CrossRefGoogle Scholar
  34. Liu M., Reed W.M. (1994). The effect of hypermedia assisted instruction on second-language learning through a semantic-network-based approach. J. Educ. Comput. Res. 12: 159–175 CrossRefGoogle Scholar
  35. Macqueen, J.B.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar
  36. Marchionini, G., Plaisant, C., Komlodi, A.: The people in digital libraries: multifaceted approaches to assessing needs and impact. In: Bishop, A., van House, N.A., Buttenfield, B.P. (eds.) Digital Library Use Social Practice in Design and Evaluation, pp. 119–160. MIT Press (2003)Google Scholar
  37. Mitchell T.J.F., Chen S.Y., Macredie R.D. (2005). Hypermedia learning and prior knowledge: domain expertise vs. system expertise. J. Comput. Assist. Learn. 21: 53–64 CrossRefGoogle Scholar
  38. Mobasher, B., Dai, H. Luo, T., Nakagawa, M.: Effective personalization based on Association Rule discovery from web usage data. In: 3rd. ACM Workshop on Web Information and Data Management (2001)Google Scholar
  39. Nasraoui, O., Frigui, H., Joshi, A., Krishnapuram, R.: Mining Web Access Logs Using Relational Competitive Fuzzy Clustering. In: Eight International Fuzzy Systems Association World Congress – IFSA 99 (1999)Google Scholar
  40. Paliouras, G., Karkaletsis, G.V., Papathedorou. C., Spyropoulos, C.: Exploiting learning techniques for the acquisition of user stereotypes and communities. In: Proceedings of the International Conference on User Modelling (UM’99) (1999)Google Scholar
  41. Palmquist R.A., Kim K.-S. (2000). Cognitive style and on-line database search experience as predictors of Web search performance. J. Am. Soc. Inform. Sci. 51: 558–566 CrossRefGoogle Scholar
  42. Park T.I., Yi S.G., Lee S., Lee S.Y., Yoo D.H., Ahn J.I., Lee Y.S. (2003). Statistical tests for identifying differentially expressed genes in time-course microarray experiments. Bioinformatics 19: 694–703 CrossRefGoogle Scholar
  43. Riding, R.J.: Cognitive Styles Analysis. Learning and Training Technology. Birmingham (1991)Google Scholar
  44. Riding R.J., Grimley M. (1999). Cognitive style, gender and learning from multimedia materials in 11 year-old children. Br. J. Educ. Technol. 30(1): 43–56 CrossRefGoogle Scholar
  45. Riding R., Rayner S.G. (1998). Cognitive Styles and Learning Strategies. David Fulton Publisher, London Google Scholar
  46. Roy M., Chi M.T.C. (2003). Gender differences in patterns of searching the web. J. Educ. Comput. Res. 29: 335–348 CrossRefGoogle Scholar
  47. Sander J., Ng R.T., Sleumer M.C., Yuen M.S., Jones S.J. (2005). A methodology for analyzing SAGE libraries for cancer profiling. ACM Trans. Inform. Syst. 23: 35–60 CrossRefGoogle Scholar
  48. Savaresi, S.M., Gazzaniga, G., Boley, D.L., Bittani, S.: Cluster selection in divisive clustering algorithms. In: Second SIAM International Conference in Data Mining, Arlington VA, pp. 85–101 (2002)Google Scholar
  49. Shapira B., Shoval P., Hanani U. (1997). Stereotypes in information filtering systems. Inform. Process. Manag. 33: 273–287 CrossRefGoogle Scholar
  50. Stephen P., Hornby S. (1997). Simple Statistics for Library and Information Professionals. Library Association, London Google Scholar
  51. Swift, S., Tucker, A., Vinciotti, V., Marin, N., Orengo, C., Liu, X., Kellam, P.: Consensus clustering and functional interpretation of gene-expression data. Genome Biol. (2004) 5:R94, Scholar
  52. Torkzadeh G., Van Dyke T.P. (2002). Effects of training on Internet self-efficacy and computer user attitudes. Comput. Human Behav. 18: 479–494 CrossRefGoogle Scholar
  53. Tarpin-Bernard F., Habieb-Mammar H. (2005). Modeling elementary cognitive abilities for adaptive hypermedia presentation. User Model. User-Adapted Interact. 15: 459–495 CrossRefGoogle Scholar
  54. Uebersax J.S. (1987). Diversity of decision-making models and the measurement of interrater agreement. Psycol. Bull. 101: 140–146 CrossRefGoogle Scholar
  55. Valiquette C., Lesage A., Cyr M., Toupin J. (1994). Computing Cohen’s kappa coefficients using SPSS MATRIX. Behav. Res. Methods, Instrum. Comput. 26: 60–61 Google Scholar
  56. Venkatesh V. (2000). Determinants of perceived ease of use: integration control, intrinsic motivation and emotion into the technology acceptance model. Inform. Syst. Res. 11: 342–365 CrossRefGoogle Scholar
  57. Wang P., Hawk W.B., Tenopir C. (2000). User’s interaction with world wide web resources: an exploratory study using a holistic approach. Inform. Process. Manag. 36: 229–251 CrossRefGoogle Scholar
  58. Webb, A.: Statistical Pattern Recognition. Arnold (1999)Google Scholar
  59. Weller H.G., Repman J., Rooze G.E. (1994). The relationship of learning, behavior, and cognitive styles in hypermedia-based instruction: Implications for design of HBI. Comput. Schools, 10: 401–420 CrossRefGoogle Scholar
  60. Witkin H.A., Moore C.A., Goodenough D.R., Cox P. (1977). Field-dependent and field independent cognitive styles and their educational implications. Rev. Educ. Res. 47: 1–64 CrossRefGoogle Scholar
  61. Witten, I.H., Frank, E.: Data Mining. Practical Machine Learning Tools and Techniques with JAVA Implementations. Morgan Kaufman Publishers (1999)Google Scholar
  62. Wolfinger R.D., Gibson G., Wolfinger E.D., Bennet, L. Hamadeh H., Buschel P., Afshari C., Paules R.S. (2001). Assesing gene significance from cDNA microarray expression data via mixed models. J. Comput. Biol. 8: 625–637 CrossRefGoogle Scholar
  63. Yi M.Y., Hwang Y. (2003). Predicting the use of web-based information systems, self-efficacy, enjoyment, learning goal orientation and the technology acceptance model. Int. J. Human-Comput. Stud. 59: 431–449 CrossRefGoogle Scholar
  64. Zukerman, I., Albrecht, D.W., Nicholson, A.E.: Predicting users request on the WWW. In: Proceedings of the 7th International Conference on User Modeling, UM99, Banff, Canada, pp. 275–284 (1999)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Enrique Frias-Martinez
    • 1
  • Sherry Y. Chen
    • 1
    Email author
  • Robert D. Macredie
    • 1
  • Xiaohui Liu
    • 1
  1. 1.Department of Information Systems and ComputingBrunel UniversityUxbridgeUK

Personalised recommendations