Cognitive Science forWeb Usage Analysis

Part of the Studies in Computational Intelligence book series (SCI, volume 452)

Abstract

Web usage mining is the process of extracting patterns from web user’s preferences and browsing behavior. Furthermore, the web user behavior refers to the user’s activities in a web site. Cognitive science is a multi-disciplinary approach used for the understanding of human behavior, whose aims is to develop models of information processing in the real brain. Therefore, cognitive sciences can have direct application to web usage mining. In this chapter, some state-of-the-art psychology theories are presented in the context of web usage analysis. In spite of the complexity of neural processes in the brain, stochastic models based on diffusion can be used to explain a decision-making process, and this has been experimentally tested. Diffusion models and theirs application to describe web usage are reviewed in this chapter. An example of application of cognitive science to web usage mining is also presented.

Keywords

Superior Colliculus Expect Utility Theory Primary Somatosensory Cortex Middle Temporal Sequential Probability Ratio Test 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Abraham, A., Ramos, V.: Web usage mining using artificial ant colony clustering and genetic programming. In: Procs. of the 2003 IEEE Congress on Evolutionary Computation, CEC 2003, pp. 1384–1391 (2003)Google Scholar
  2. 2.
    Akiva, M.B., Lerman, S.: Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press (1995)Google Scholar
  3. 3.
    Amos, A.: A computational model of information processing in the frontal cortex and basal ganglia. Journal of Cognitive Neuroscience 12(3), 505–519 (2000)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Anderson, C.: Wired Magazine, Editorial (June 2008)Google Scholar
  5. 5.
    Anderson, C.R., Domingos, P., Weld, D.S.: Web site personalizers for mobile devices. In: The IJCAI Workshop on Inteligent Techniques for Web Personalization, ITWP 2001 (2001)Google Scholar
  6. 6.
    Anderson, J.R., Bothell, D., Byrne, M.D., Douglass, S., Lebiere, C., Qin, Y.: An integrated theory of the mind. Psychological Review 111(4), 1036–1060 (2004)CrossRefGoogle Scholar
  7. 7.
    Audley, R.J., Pike, A.R.: Some alternative stochastic models of choice. British Journal of Mathematical and Statistical Psychology 18, 207–225 (1965)CrossRefGoogle Scholar
  8. 8.
    Basso, M.A., Wurtz, R.H.: Modulation of neuronal activity in superior colliculus by changes in target probability. J. Neurosci. 18(18), 7519–7534 (1998)Google Scholar
  9. 9.
    Bhatnagar, V., Gupta, S.K.: Modeling the kdd process. In: Encyclopedia of Data Warehousing and Mining, pp. 1337–1345. IRMA International (2009)Google Scholar
  10. 10.
    Blum, A., Chan, H., Rwebangira, M.R.: A random-surfer web-graph model. In: Proceedings of the Eigth Workshop on Algorithm Engineering and Experiments and the Third Workshop on Analytic Algorithmics and Combinatorics, pp. 238–246. Society for Industrial and Applied Mathematics (2006)Google Scholar
  11. 11.
    Bogacz, R., Brown, E., Moehlis, J., Holmes, P., Cohen, J.D.: The physics of optimal decision making: A formal analysis of models of performance in two-alternative forced choice tasks. Psychological Review 4(113), 700–765 (2006)CrossRefGoogle Scholar
  12. 12.
    Borges, J.: A Data Mining Model to Capture User Web Navigation Patterns. PhD thesis, London University (2000)Google Scholar
  13. 13.
    Borooah, V.K.: Logit and probit: ordered and multinomial models, vol. 138 (2001); Quantitative applications in the social sciences. Sage Publications (2002)Google Scholar
  14. 14.
    Britten, K.H., Shadlen, M.N., Newsome, W.T., Movshon, J.A.: Response of neurons in macaque motion signals. Visual Neuroscience 9, 1157–1169 (2006)Google Scholar
  15. 15.
    Buckner, R.L., Kelley, W.M., Petersen, S.E.: Frontal cortex contributes to human memory formation. Nature Neuroscience 2, 311–314 (1999)CrossRefGoogle Scholar
  16. 16.
    Busemeyer, J.R., Diederich, A.: Survey of decision field theory. Mathematical Social Sciences 43(3), 345–370 (2002)MathSciNetMATHCrossRefGoogle Scholar
  17. 17.
    Busemeyer, J.R., Jessup, R.K., Johnson, J.G., Townsend, J.T.: Building bridges between neural models and complex decision making behaviour. Neural Networks 19(8), 1047–1058 (2006); Neurobiology of Decision MakingMATHCrossRefGoogle Scholar
  18. 18.
    Busemeyer, J.R., Pothos, E.M., Franco, R.: A quantum theoretical explanation for probability judgment errors. Psychology Revue Letter (2010) (submitted)Google Scholar
  19. 19.
    Busemeyer, J.R., Townsend, J.T.: Decision field theory: a dynamic-cognitive approach to decision making in an uncertain environment. Psychological Review 100(3), 432–459 (1993)CrossRefGoogle Scholar
  20. 20.
    Busemeyer, J.R., Wang, Z., Townsend, J.T.: A quantum dynamics of human decision making. Journal of Mathematical Psychology 50(3), 220–241 (2006)MathSciNetMATHCrossRefGoogle Scholar
  21. 21.
    Cascetta, E.: Transportation systems engineering: theory and methods. Applied Optimization. Kluwer Academic Publishers (2001)Google Scholar
  22. 22.
    Chanceaux, M., Guérin-Dugué, A., Lemaire, B., Baccino, T.: A Model to Simulate Web Users’ Eye Movements. In: Gross, T., Gulliksen, J., Kotzé, P., Oestreicher, L., Palanque, P., Prates, R.O., Winckler, M. (eds.) INTERACT 2009, Part I. LNCS, vol. 5726, pp. 288–300. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  23. 23.
    Churchland, A.K., Kiani, R., Shadlen, M.N.: Decision making with multiple choice. Nature Neuroscience 11(6), 693–702 (2008)CrossRefGoogle Scholar
  24. 24.
    Cutrell, E., Guan, Z.: What are you looking for?: an eye-tracking study of information usage in web search. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2007, pp. 407–416. ACM, New York (2007)CrossRefGoogle Scholar
  25. 25.
    de Haan, L., Ferreira, A.: Extreme value theory: an introduction. Springer (2006)Google Scholar
  26. 26.
    de la Cruz Martínez, G., Rodríguez, F.G.: Using user interaction to model user comprehension on the web navigation. International Journal of Computer Information Systems and Industrial Management Applications 3, 878–885 (2011)Google Scholar
  27. 27.
    Eliasmith, C.: Computational neuroscience. In: Thagard, P. (ed.) Handbook of Philosophy of Science, vol. 4, pp. 313–338. Elsevier (2007)Google Scholar
  28. 28.
    Emerson, P.L.: Simple reaction time with markovian evolution of gaussian discriminal processes. Psychometrika 35(1), 99–109 (1970)MATHCrossRefGoogle Scholar
  29. 29.
    Farzan, R., Brusilovsky, P.: Social Navigation Support for Information Seeking: If You Build It, Will They Come? In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 66–77. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  30. 30.
    Fasolo, B., McClelland, G.H., Lange, K.A.: The effect of site design and interattribute correlations on interactive web-based decisions. In: Online Consumer Psychology: Understanding and Influencing Behavior in the Virtual World, pp. 325–344. Psychology Press (2005)Google Scholar
  31. 31.
    Glimcher, P.W., Camerer, C., Poldrack, R.A.: Neuroeconomics: Decision Making and the Brain. Academic Press (2008)Google Scholar
  32. 32.
    Gold, J., Shadlen, M.: The neural basis of decision making. Annual Review of Neuroscience 30, 535–574 (2007)CrossRefGoogle Scholar
  33. 33.
    Google. Investor relation: Finacial table (2009), http://investor.google.com/financial/tables.html
  34. 34.
    Grossberg, S., Gutowski, W.E.: Neural dynamics of decision making under risk: Affective balance and cognitive-emotional interactions. Psychological Review 94(3), 300–318 (1987)CrossRefGoogle Scholar
  35. 35.
    Hanks, T.D., Ditterich, J., Shadlen, M.N.: Microstimulation of macaque area lip affects decision-making in a motion discrimination task. Nature Neuroscience 9(5), 682–689 (2006)CrossRefGoogle Scholar
  36. 36.
    Hebb, D.: The organization of behaviour: a neuropsychological theory. L. Erlbaum Associates (1949)Google Scholar
  37. 37.
    Heekeren, H.R., Marrett, S., Ungerleider, L.G.: The neural systems that mediate human perceptual decision making. Nature Reviews Neuroscience 9, 467–479 (2008)CrossRefGoogle Scholar
  38. 38.
    Helander, M.G., Khalid, H.M.: Modeling the customer in electronic commerce. Applied Ergonomics 31, 609–619 (2000)CrossRefGoogle Scholar
  39. 39.
    Hill, A.V.: Excitation and accommodation in nerve. Proceedings of the Royal Society B 119, 305–355 (1936)CrossRefGoogle Scholar
  40. 40.
    Huberman, B.A., Pirolli, P.L.T., Pitkow, J.E., Lukose, R.M.: Strong regularities in world wide web surfing. Science 280(5360), 95–97 (1998)CrossRefGoogle Scholar
  41. 41.
    Jimenez-Molina, A., Ko, I.-Y.: Cognitive resource aware service provisioning. In: The 2011 IEEE / WIC / ACM International Conference (2011) (to appear)Google Scholar
  42. 42.
    Kahneman, D.: Maps of Bounded Rationality:a perspective on intuitive judgment and choice. In: The Nobel Prizes 2002. Nobel Foundation (2003)Google Scholar
  43. 43.
    Kahneman, D., Tversky, A.: Prospect theory: An analysis of decision under risk. Econometrica 47, 263–291 (1979)MATHCrossRefGoogle Scholar
  44. 44.
    Karampatziakis, N., Paliouras, G., Pierrakos, D., Stamatopoulos, P.: Navigation Pattern Discovery Using Grammatical Inference. In: Paliouras, G., Sakakibara, Y. (eds.) ICGI 2004. LNCS (LNAI), vol. 3264, pp. 187–198. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  45. 45.
    Karanam, S., Van Oostendorp, H., Indurkhya, B.: The role of content in addition to hyperlinks in user-clicking behavior. In: Proceedings of the 28th Annual European Conference on Cognitive Ergonomics, ECCE 2010, pp. 125–131. ACM, New York (2010)CrossRefGoogle Scholar
  46. 46.
    Kiani, R., Shadlen, M.N.: Representation of confidence associated with a decision by neurons in the parietal cortex. Science 324(8), 759–764 (2008)CrossRefGoogle Scholar
  47. 47.
    Kim, J.-N., Shadlen, M.N.: Neural correlates of a decision in the dorsolateral prefrontal cortex of the macaque. Nature Neuroscience 2(2), 176–185 (1999)CrossRefGoogle Scholar
  48. 48.
    Kitajima, M., Polson, P.G., Blackmon, M.H.: Colides and snifact: Complementary models for searching and sensemaking on the web. In: Human Computer Interaction Consortium (HCIC) Winter Workshop (2007)Google Scholar
  49. 49.
    Kitajima, M., Blackmon, M.H., Polson, P.G.: Cognitive Architecture for Website Design and Usability Evaluation: Comprehension and Information Scent in Performing by Exploration. In: Proceedings of the Human Computer Interaction International Conference (2005)Google Scholar
  50. 50.
    Korfiatis, G., Paliouras, G.: Modeling web navigation using grammatical inference. Applied Artificial Intelligence 22(1&2), 116–138 (2008)CrossRefGoogle Scholar
  51. 51.
    Laberge, D.: A recruitment theory of simple behavior. Psychometrika 27(4), 375–396 (1979)CrossRefGoogle Scholar
  52. 52.
    Laming, D.R.J.: Information theory of choice reaction time. Wiley (1968)Google Scholar
  53. 53.
    Lohr, S.: A 1 million dollars research bargain for netflix, and maybe a model for others. New York Times (2009)Google Scholar
  54. 54.
    Loyola, P., Román, P.E., Velásquez, J.D.: Clustering-based learning approach for ant colony optimization model to simulate web user behavior. In: 2011 IEEE / WIC / ACM International Conference, France (2011)Google Scholar
  55. 55.
    Luna, R., Hernandez, A., Broddy, C.D., Romo, R.: Neural codes for perceptual discrimination in primary somatosensory cortex. Nature Neuroscience 8(9), 1210–1219 (2005)CrossRefGoogle Scholar
  56. 56.
    Manning, C.D., Schutze, H.: Fundation of Statistical Natural Language Processing. The MIT Press (1999)Google Scholar
  57. 57.
    Mas-Colell, A., Whinston, M.D., Green, J.R.: Microeconomic Theory. Oxford University Press (1985)Google Scholar
  58. 58.
    Mcclelland, J.L.: Toward a theory of information processing in graded, random, and interactive network. In: Meyer, D.E., Kornblum (eds.) Attention and Performance XIV: Synergies in Experimental Psychology, Artificial Intelligence, and Cognitive Neuroscience, pp. 665–688. MIT Press (1993)Google Scholar
  59. 59.
    McFadden, D.: Is conditional logit analysis of qualitative choice behavior. In: Zarembkaá (ed.) Frontiers in Econometrics. Academic Press (1973)Google Scholar
  60. 60.
    Meyer, D.E., Irwin, D.E., Osman, A.M., Kounios, J.: The dynamics of cognition and action: mental processes inferred from speed-accuracy decomposition. Psychol. Rev. 95(2), 183–237 (1988)CrossRefGoogle Scholar
  61. 61.
    Miller, C.S., Remington, R.W.: Modeling information navigation: implications for information architecture. Human Computer Interaction 19(3), 225–271 (2004)CrossRefGoogle Scholar
  62. 62.
    Navon, D.: On the economy of the human-processing system. Psychological Review 86(3), 214–255 (1979)CrossRefGoogle Scholar
  63. 63.
    O’Reilly, R.C.: The what and how of prefrontal cortical organization. Trends in Neuroscience 33(8), 355–361 (2010)CrossRefGoogle Scholar
  64. 64.
    Philiastides, M.G., Heekeren, H.R.: Spatiotemporal characteristics of perceptual decision making in the human brain. In: Dreher, D.J.-C., Tremblay, L. (eds.) Handbook of Reward and Decision Making, pp. 185–212. Academic Press, New York (2009)CrossRefGoogle Scholar
  65. 65.
    Pirolli, P.L.T.: Information Foraging Theory: Adaptive Interaction with Information. Oxford University Press (2007)Google Scholar
  66. 66.
    Pirolli, P.L.T.: Power of 10: Modeling complex information-seeking systems at multiple scales. Computer 42, 33–40 (2009)CrossRefGoogle Scholar
  67. 67.
    Pirolli, P.L.T., Fu, W.-T.: Snifact: a model of information foraging on the world wide web. In: 9th International Conference on User Modeling (2003)Google Scholar
  68. 68.
    Pothos, E.M., Busemeyer, J.R.: A quantum probability explanation for violation of rational decision theory. Proceedings of The Royal Society B 276(1165), 2171–2178 (2009)CrossRefGoogle Scholar
  69. 69.
    Ratcliff, R.: A theory of memory retrieval. Psychological Review 85(2), 59–108 (1978)CrossRefGoogle Scholar
  70. 70.
    Ratcliff, R., Van Zandt, T., McKoon, G.: Connectionist and diffusion models of reaction time. Psychological Revue. 106(2), 261–300 (1999)CrossRefGoogle Scholar
  71. 71.
    Resnick, S.I.: Adventures in stochastic processes. Birkhauser Verlag, Basel (1992)MATHGoogle Scholar
  72. 72.
    Rieskamp, J., Busemeyer, J.R., Mellers, B.A.: Extending the bounds of rationality: Evidence and theories of preferential choice. Journal of Economic Literature 44(3), 631–661 (2006)CrossRefGoogle Scholar
  73. 73.
    Roitman, J.D., Shadlen, M.N.: Response of neurons in the lateral intraparietal area during a combined visual discrimination reaction time task. Journal of Neuroscience 22, 9475–9489 (2002)Google Scholar
  74. 74.
    Román, P.E.: Web User Behavior Analysis. PhD thesis, University of Chile (January 2011)Google Scholar
  75. 75.
    Román, P.E., Velásquez, J.D.: Analysis of the web user behavior with a psychologically-based diffusion model. In: Of the AAAI, Fall Symposium on Biologically Inspired Cognitive Architectures, Arlington, USA., Arlington, Washington DC, USA, Technical Paper of the AAAI (2009)Google Scholar
  76. 76.
    Román, P.E., Velásquez, J.D.: A dynamic stochastic model applied to the analysis of the web user behavior. In: Snasel, et al. (eds.) The 2009 AWIC 6th Atlantic Web Intelligence Conference, Prague, Czech Republic. Invited Lecture. Intelligent and Soft Computing Series, Advances in Intelligent Web Mastering-2, pp. 31–40 (2009)Google Scholar
  77. 77.
    Román, P.E., Velásquez, J.D.: Artificial web user simulation and web usage mining. In: The First Workshop in Business Analytics and Optimizatio áBAO 2010, Santiago, Chile (January 2010)Google Scholar
  78. 78.
    Román, P.E., Velásquez, J.D.: Stochastic simulation of web users. In: Procs. of the 2010 IEEE / WIC / ACM International Conference, Toronto, Canada. IEEE Press (September 2010)Google Scholar
  79. 79.
    Román, P.E., Velásquez, J.D.: The time course of the web user. In: Second Workshop on Time Use Observatory áTUO2, San Felipe, Chile (March 2010)Google Scholar
  80. 80.
    Rubinstein, R.Y., Kroese, D.P.: Simulation and the Monte Carlo method. Wiley series in probability and mathematical statistics. Probability and mathematical statistics. John Wiley & Sons (2008)Google Scholar
  81. 81.
    Sato, T., Murthy, A., Thompson, K.G., Schall, J.D.: Search efficiency but not response interference affects visual selection in frontal eye field. Neuron 30, 583–591 (2001)CrossRefGoogle Scholar
  82. 82.
    Schall, J.D.: Neural basis of deciding, choosing and acting. National Review of Neuroscience 2(1), 33–42 (2001)CrossRefGoogle Scholar
  83. 83.
    Schall, J.D.: On building a bridge between brain and behavior. Annual Review of Psychology 55, 23–50 (2004)CrossRefGoogle Scholar
  84. 84.
    Schall, J.D.: Frontal eye fields. In: Encyclopedia of Neuroscience, vol. 4, pp. 367–374. Elsevier (2009)Google Scholar
  85. 85.
    Scott, M.L.: Programming language pragmatics. Morgan Kaufmann Publishers Inc., San Francisco (2000)Google Scholar
  86. 86.
    Stone, M.: Models for choice reaction time. Psychometrika 25(3), 251–260 (1960)MATHCrossRefGoogle Scholar
  87. 87.
    Strogatz, S.H.: Romanesque networks. Nature 433(27), 365–366 (2005)CrossRefGoogle Scholar
  88. 88.
    Michael Jahrer, A.T., Bell, R.M., Park, F.: The bigchaos solution to the netflix grand prize. NetFlix, 1–52 (2009)Google Scholar
  89. 89.
    Telang, R., Kumar, A.: Impact of customer web portals on call center: An empirical analysis. SSRN eLibrary (2009)Google Scholar
  90. 90.
    Train, K.: Discrete choice methods with simulation. Cambridge University Press (2009)Google Scholar
  91. 91.
    Tversky, A., Kahneman, D.: The framing of decisions and the psychology of choice. Science 211(4481), 453–458 (1981)MathSciNetMATHCrossRefGoogle Scholar
  92. 92.
    Tversky, A., Sattath, S.: Preference trees. Psychological Review 86(6), 542–573 (1979)CrossRefGoogle Scholar
  93. 93.
    Tversky, A., Simonson, I.: Context-dependent preferences. Management Science 39(10), 1179–1189 (1993)MATHCrossRefGoogle Scholar
  94. 94.
    Usher, M., McClelland, J.L.: The time course of perceptual choice: The leaky, competing accumulator model. Psychological Review 2(1), 550–592 (2001)CrossRefGoogle Scholar
  95. 95.
    Velasquez, J.D., Palade, V.: A knowledge base for the maintenance of knowledge. Journal of Knowledge Based Systems 1(20), 238–248 (2007)CrossRefGoogle Scholar
  96. 96.
    Van Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior (Commemorative Edition) (Princeton Classic Editions), 60 anv edn. Princeton University Press (2007)Google Scholar
  97. 97.
    Wald, A., Wolfowitz, J.: Optimum character of the sequential probability ratio test. The Annals of Mathematical Statistics 19(3), 326–339 (1948)MathSciNetMATHCrossRefGoogle Scholar
  98. 98.
    Wickens, C.D.: Multiple resources and performance prediction. Theoretical Issues in Ergonomics Science 3(2), 159–177 (2002)CrossRefGoogle Scholar
  99. 99.
    Wickens, C.D.: Multiple Resources and Mental Workload. Hum. Factors 50(3), 449–455 (2008)CrossRefGoogle Scholar
  100. 100.
    Wickens, T.D.: Elementary Signal Detection Theory. Oxford University Press (2002)Google Scholar
  101. 101.
    Zeng, L.: A heteroscedastic generalized extreme value discrete choice model. Sociological Methods Research 29(1), 118–144 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Web Intelligence Consortium Chile Research Centre, Department of Industrial Engineering School of Engineering and ScienceUniversity of ChileSantiagoChile

Personalised recommendations