Web usage mining

Studies in Computational Intelligence

Volumn 311, Issue , 2010, Pages 143-165

  Román, Pablo E ^a   L'Huillier, Gastón ^a   Velásquez, Juan D ^a  

^a UNIVERSITY OF CHILE   (Chile)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77956546081     PISSN: 1860949X     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-642-14461-5_6     Document Type: Article

References (93)

1. Ayyagari, P., Sun, Y. Modeling the internet and the web: Probabilistic methods and algorithms. by pierre baldi, paolo frasconi, padhraic smith, john wiley and sons ltd., west sussex, england, 2003. 285 pp isbn 0 470 84906 1. Inf. Process. Manage. 42(1), 325-326 (2006)
2. Baeza-yates, R., Castillo, C. Crawling the infinite web: Five levels are enough. In: Leonardi, S. (ed.) WAW 2004. LNCS, vol. 3243, pp. 156-167. Springer, Heidelberg (2004)
3. Berendt, B., Hotho, A., Stumme, G. Data preparation for mining world wide web browsing patterns. Journal of Knowlegde and Information Systems 1(1), 5-32 (1999)
4. Berendt, B., Mobasher, B., Spiliopoulou, M., Wiltshire, J. Measuring the accuracy of sessionizers for web usage analysis. In: Proc. of the Workshop on Web Mining, First SIAM Internat.Conf. on Data Mining, pp. 7-14 (2001)
5. Bixby, R.E. Solving real-world linear programs: A decade and more of progress. Operations Research 50(1), 3-15 (2002)
6. Borges, J., Levene, M. Data mining of user navigation patterns. In: WEBKDD 1999: Revised Papers from the International Workshop on Web Usage Analysis and User Profiling, London, UK, pp. 92-111. Springer, Heidelberg (2000)
7. Borges, J., Levene, M. Evaluating variable-length markov chain models for analysis of user web navigation sessions. IEEE Trans. on Knowl. and Data Eng. 19(4), 441-452 (2007)
8. Burget, R., Rudolfova, I. Web page element classification based on visual features. In: Asian Conference on Intelligent Information and Database Systems, vol. 0, pp. 67-72 (2009)
9. Cadez, I., Heckerman, D., Meek, C., Smyth, P., White, S. Visualization of navigation patterns on a web site using model-based clustering. In: KDD 2000: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 280-284. ACM, New York (2000)
10. Chakrabarti, D., Faloutsos, C. Graph mining: Laws, generators, and algorithms. ACM Comput. Surv. 38(1), 2 (2006)
11. Chen, X., Zhang, X. A popularity-based prediction model for web prefetching. Computer 36(3), 63-70 (2003)
12. Cooley, R., Mobasher, B., Srivastava, J. Towards semantic web mining. In: Proc. in First Int. Semantic Web Conference, pp. 264-278 (2002)
13. Dell, R.F., Roḿan, P.E., Veĺasquez, J.D. Web user session reconstruction using integer programming. In: Procs. of The 2008 IEEE/WIC/ACMInternational Conference onWeb Intelligence, Sydney, Australia, December 2008, pp. 385-388 (2008)
14. Demir, G.N., Uyar, A.S., Ögüdücü, S.G. Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systems. In: GECCO 2007: Proceedings of the 9th annual conference on Genetic and evolutionary computation, pp. 1943-1950. ACM, New York (2007)
15. Dongshan, X., Junyi, S. A new markov model for web access prediction. Computing in Science and Engg. 4(6), 34-39 (2002)
16. Dujovne, L.E., Veĺasquez, J.D. Design and implementation of a methodology for identifying website keyobjects. In: Veĺasquez, J.D., Ŕos, S.A., Howlett, R.J., Jain, L.C. (eds.) Knowledge-Based and Intelligent Information and Engineering Systems. LNCS, Part I, vol. 5711, pp. 301-308. Springer, Heidelberg (2009)
17. Eirinaki, M., Vazirgiannis, M., Kapogiannis, D. Web path recommendations based on page ranking and markov models. In: WIDM 2005: Proceedings of the 7th annual ACM international workshop on Web information and data management, pp. 2-9. ACM, New York (2005)
18. Facca, F., Lanzi, P. Recent developments in web usage mining research. In: Kambayashi, Y.,Mohania, M.,Wöß,W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 140-150. Springer, Heidelberg (2003)
19. Felzenszwalb, P.F., Huttenlocher, D.P., Kleinberg, J.M. Fast algorithms for largestate-space hmms with applications to web usage analysis. In: Thrun, S., Saul, L.K., Schölkopf, B. (eds.) NIPS. MIT Press, Cambridge (2003)
20. Glassman, S. A caching relay for the world wide web. In: Selected papers of the first conference on World-Wide Web, pp. 165-173. Elsevier Science Publishers B. V., Amsterdam (1994)
21. Grannis, K., Davis, E. Online sales to climb despite struggling economy, According to Shop.org/Forrester Research Study (2008)
22. Grannis, K., Davis, E. China internet network information center. In: 14th statistical survey report on the internet development of china (2009), http://www.cnnic.net.cn/uploadfiles/pdf/2009/10/13/94556.pdf
23. Gündüz, Ş., Özsu, M.T. A web page prediction model based on click-stream tree representation of user behavior. In: KDD 2003: Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 535-540. ACM, New York (2003)
24. Guo, Y.Z., Ramamohanarao, K., Park, L.A. Grouped ecoc conditional random fields for prediction of web user behavior. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, T.-B. (eds.) PAKDD 2009. LNCS, vol. 5476, pp. 757-763. Springer, Heidelberg (2009)
25. Guo, Y.Z., Ramamohanarao, K., Park, L.A.F.Web page prediction based on conditional random fields. In: Proceeding of the 2008 conference on ECAI 2008, pp. 251-255. IOS Press, Amsterdam (2008)
26. Hasan, T., Mudur, S.P., Shiri, N. A session generalization technique for improved web usage mining. In: WIDM 2009: Proceeding of the eleventh international workshop on Web information and data management, pp. 23-30. ACM, New York (2009)
27. Hay, B., Wets, G., Vanhoof, K. Mining navigation patterns using a sequence alignment method. Knowl. Inf. Syst. 6(2), 150-163 (2004)
28. Hongwei, W., Xie, L. Adaptive site design based on web mining and topology. In: CSIE 2009: Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering, Washington, DC, USA, pp. 184-189. IEEE Computer Society, Los Alamitos (2009)
29. Huberman, B., Pirolli, P., Pitkow, J., Lukose, R.M. Strong regularities in world wide web surfing. Science 280(5360), 95-97 (1998)
30. Huntington, P., Nicholas, D., Jamali, H.R. Website usage metrics: A re-assessment of session data. Information Processing & Management 44(1), 358-372 (2008)
31. Hannah Inbarani, H., Thangavel, K., Pethalakshmi, A. Rough set based feature selection for web usage mining. In: ICCIMA2007: Proceedings of the International Conference on Computational Intelligence and Multimedia Applications (ICCIMA 2007), Washington, DC, USA, pp. 33-38. IEEE Computer Society, Los Alamitos (2007)
32. Ingwersen, P., Jirvelin, K. The Turn: Integration of Information Seeking and Retrieval in Context, 1st edn. Springer, Heidelberg (2005)
33. Jespersen, S., Pedersen, T.B., Thorhauge, J. Evaluating the markov assumption for web usage mining. In: WIDM 2003: Proceedings of the 5th ACM international workshop on Web information and data management, pp. 82-89. ACM, New York (2003)
34. Jin, X., Zhou, Y.,Mobasher, B.Web usage mining based on probabilistic latent semantic analysis. In: KDD 2004: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, New York (2004)
35. Joshi, A., Krishnapuram, R. On mining web access logs. In: Proc. of the 2000 ACM SIGMOD Workshop on Research Issue in Data Mining and Knowledge Discovery, pp. 63-69 (2000)
36. Juels, A., Jakobsson, M., Jagatic, T.N. Cache cookies for browser authentication (extended abstract). In: SP 2006: Proceedings of the 2006 IEEE Symposium on Security and Privacy, Washington, DC, USA, pp. 301-305. IEEE Computer Society, Los Alamitos (2006)
37. Kausshik, A.Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity. Sybex (2009)
38. Kellar, M. An Examination of User Behaviour during Web Information Tasks. PhD thesis, Dalhousie University, Halifax, Nova Scotia, Canada (2007)
39. Kolias, C., Kolias, V., Anagnostopoulos, I., Kambourakis, G., Kayafas, E. Enhancing user privacy in adaptive web sites with client-side user profiles. In: SMAP 2008: Proceedings of the 2008 Third International Workshop on Semantic Media Adaptation and Personalization, Washington, DC, USA, pp. 170-176. IEEE Computer Society, Los Alamitos (2008)
40. Koren, Y., Bell, R., Volinsky, C. Matrix factorization techniques for recommender systems. Computer 42(8), 30-37 (2009)
41. Lafferty, J.D., McCallum, A., Pereira, F.C.N. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: ICML 2001: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 282-289. Morgan Kaufmann Publishers Inc., San Francisco (2001)
42. Lan, M., Tan, C.L., Su, J., Lu, Y. Supervised and traditional term weighting methods for automatic text categorization. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 721-735 (2009)
43. Langford, D. Internet ethics. MacMillan Press Ltd., Basingstoke (2000)
44. Levene, M., Borges, J., Loizou, G. Zipfs law for web surfers. Knowl. Inf. Syst. 3(1), 120-129 (2001)
45. Liu, J., Zhang, S., Yang, J. Characterizing web usage regularities with information foraging agents. IEEE Trans. on Knowl. and Data Eng. 16(5), 566-584 (2004)
46. Lohr, S. A 1 million dollars research bargain for netflix, and maybe a model for others. New York Times (2009)
47. Loyola, P., Roḿan, P.E., Veĺasquez, J.D. Colony surfer: Discovering the distribution of text preferences from web usage. In: Procs. Of the FirstWorkshop in Business Analytics and Optimization, BAO (2010)
48. Manning, C.D., Schutze, H. Fundation of Statistical Natural Language Processing. The MIT Press, Cambridge (1999)
49. Masseglia, F., Poncelet, P., Teisseire, M., Marascu, A. Web usage mining: extracting unexpected periods from web logs. Data Min. Knowl. Discov. 16(1), 39-65 (2008)
50. Mayer-Schonberger, V. Nutzliches vergessen. In: Goodbye privacy grundrechte in der digitalen welt (Ars Electronica), pp. 253-265 (2008)
51. Mikroyannidis, A., Theodoulidis, B. Heraclitus: A framework for semantic web adaptation. IEEE Internet Computing 11(3), 45-52 (2007)
52. Mobasher, B., Cooley, R., Srivastava, J. Creating adaptive web sites through usage-based clustering of urls. In: KDEX 1999: Proceedings of the 1999 Workshop on Knowledge and Data Engineering Exchange, Washington, DC, USA, p. 19. IEEE Computer Society, Los Alamitos (1999)
53. Mobasher, B., Cooley, R., Srivastava, J. Automatic personalization based on web usage mining. Commun. ACM 43(8), 142-151 (2000)
54. Mobasher, B., Dai, H., Luo, T., Nakagawa, M. Effective personalization based on association rule discovery from web usage data. In: WIDM 2001: Proceedings of the 3rd international workshop onWeb information and data management, pp. 9-15. ACM, New York (2001)
55. Mobasher, B., Dai, H., Luo, T., Nakagawa, M. Discovery and evaluation of aggregate usage profiles for web personalization. Data Min. Knowl. Discov. 6(1), 61-82 (2002)
56. Obendorf, H., Weinreich, H., Herder, E., Mayer, M.Web page revisitation revisited: implications of a long-term click-stream study of browser usage. In: CHI 2007: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 597-606 (2007)
57. Olston, C., Chi, E.H. Scenttrails: Integrating browsing and searching on the web. ACM Trans. Comput.-Hum. Interact. 10(3), 177-197 (2003)
58. Park, S., Suresh, N.C., Jeong, B.-K. Sequence-based clustering for web usage mining: A new experimental framework and ann-enhanced k-means algorithm. Data Knowl. Eng. 65(3), 512-543 (2008)
59. Paterek, A. Improving regularized singular value decomposition for collaborative filtering. In: Proceedings of KDD Cup and Workshop (2007)
60. Perkowitz, M., Etzioni, O. Towards adaptive web sites: conceptual framework and case study. Artif. Intell. 118(1-2), 245-275 (2000)
61. Plumbaum, T., Stelter, T., Korth, A. Semantic web usage mining: Using semantics to understand user intentions. In: Houben, G.-J., McCalla, G., Pianesi, F., Zancanaro, M. (eds.) UMAP 2009. LNCS, vol. 5535, pp. 391-396. Springer, Heidelberg (2009)
62. Resnick, S.I. Adventures in stochastic processes. Birkhauser Verlag, Basel (1992)
63. Ŕos, S.A., Veĺasquez, J.D. Semantic web usage mining by a concept-based approach for off-line web site enhancements. In: WI-IAT 2008: Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Washington, DC, USA, pp. 234-241. IEEE Computer Society, Los Alamitos (2008)
64. Roḿan, P.E., Veĺasquez, J.D. Markov chain for modeling web user browsing behavior: statistical inference. In: XIV Latin Ibero-American Congress on Operations Research, CLAIO (2008)
65. Roḿan, P.E., Veĺasquez, J.D. Analysis of the web user behavior with a psychologicallybased diffusion model. In: Procs. of the AAAI 2009 Fall Symposium on Biologically Inspired Cognitive Architectures, Arlington VA, USA, p. 72 (2009)
66. Roḿan, P.E., Veĺasquez, J.D. A dynamic stochastic model applied to the analysis of the web user behavior. In: The 2009 AWIC 6th Atlantic Web Intelligence Conference, Prague, Czech Republic, pp. 31-40 (2009) (Invited Lecture)
67. Runkler, T.A., Bezdek, J.C.Web mining with relational clustering. International Journal of Approximate Reasoning 32(2-3), 217-236 (2003)
68. Brin, S., Page, L. The anatomy of a large-scale hypertextual web search engine. In: Computer Networks and ISDN Systems, pp. 107-117 (1998)
69. Sarukkai, R.R. Link prediction and path analysis using markov chains. In: Proceedings of the 9th international World Wide Web conference on Computer networks: the international journal of computer and telecommunications networking, pp. 377-386. North-Holland Publishing Co., Amsterdam (2000)
70. Schmidt, K.-U., Stojanovic, L., Stojanovic, N., Thomas, S. On enriching ajax with semantics: The web personalization use case. In: Franconi, E., Kifer, M., May, W. (eds.) ESWC 2007. LNCS, vol. 4519, pp. 686-700. Springer, Heidelberg (2007)
71. Sen, R., Hansen, M. Predicting web users next access based on log data. J. Comput. Graph. Stat. 12(1), 143-155 (2003)
72. Shi, J., Shi, F.,Qiu, H. Users interests navigation model based on hidden markov model. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS (LNAI), vol. 2639, pp. 631-634. Springer, Heidelberg (2003)
73. Sńasel, V., Kudelka, M. Web content mining focused on named objects. In (IHCI) First International Conference on Intelligent Human Computer Interaction, pp. 37-58. Springer, India (2009)
74. Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M. A framework for the evaluation of session reconstruction heuristics in web-usage analysis. Informs Journal on Computing 15(2), 171-190 (2003)
75. Spiliopoulou, M., Faulstich, L. Wum: A web utilization miner. In: Atzeni, P., Mendelzon, A.O., Mecca, G. (eds.) WebDB 1998. LNCS, vol. 1590, pp. 184-203. Springer, Heidelberg (1998)
76. Srivastava, J., Cooley, R., Deshpande, M., Tan, P. Web usage mining: Discovery and applications of usage patterns from web data. SIGKDD Explorations 2(1), 12-23 (2000)
77. Taḱacs, G., Piĺaszy, I., Ńemeth, B., Tikk, D. Major components of the gravity recommendation system. SIGKDD Explor. Newsl. 9(2), 80-83 (2007)
78. Tao, Y.-H., Hong, T.-P., Lin, W.-Y., Chiu, W.-Y. A practical extension of web usage mining with intentional browsing data toward usage. Expert Syst. Appl. 36(2), 3937-3945 (2009)
79. Tao, Y.-H., Hong, T.-P., Su, Y.-M. Web usage mining with intentional browsing data. Expert Syst. Appl. 34(3), 1893-1904 (2008)
80. Vazquez, A., Gama Oliveira, J., Dezso, Z., Goh, K.-I., Kondor, I., Barabasi, A.-L. Modeling bursts and heavy tails in human dynamics. Physical Review E 73(3), 36127 (2006)
81. Vazquez, A. Exact results for the barabasi model of human dynamics. Physical Review Letters 95(24), 248701 (2005)
82. Veĺasquez, J.D., Yasuda, H., Aoki, T., Weber, R. A new similarity measure to understand visitor behavior in a web site. IEICE Transactions on Information and Systems, Special Issues in Information Processing Technology for web utilization E87-D(2), 389-396 (2004)
83. Veĺasquez, J.D., Palade, V. Adaptive web sites: A knowledge extraction from web data approach. IOS Press, Amsterdam (2008)
84. Veĺasquez, J.D., Est́evez, P.A., Yasuda, H., Aoki, T., Vera, E.S. Intelligent web site: Understanding the visitor behavior. In: Negoita, M.G., Howlett, R.J., Jain, L.C. (eds.) KES 2004. LNCS (LNAI), vol. 3213, pp. 140-147. Springer, Heidelberg (2004)
85. Veĺasquez, J.D., Weber, R., Yasuda, H., Aoki, T. A methodology to find web site keywords. In: EEE 2004: Proceedings of the 2004 IEEE International Conference on e-Technology, e-Commerce and e-Service (EEE 2004), Washington, DC, USA, pp. 285-292. IEEE Computer Society, Los Alamitos (2004)
86. Wang, S., Gao,W., Huang, T.,Ma, J., Li, J., Xie, H. Adaptive online retail web site based on hidden markov model. In: Lu, H., Zhou, A. (eds.) WAIM 2000. LNCS, vol. 1846, pp. 177-188. Springer, Heidelberg (2000)
87. White, R.W. Investigating behavioral variability in web search. In: Proc. WWW, pp. 21-30 (2007)
88. White, R.W., Drucker, S.M. Investigating behavioral variability in web search. In: WWW 2007: Proceedings of the 16th international conference on World Wide Web (2007)
89. Wolpert, D.H. Stacked generalization. Neural Networks 5, 241-259 (1992)
90. Xexeo, G., de Souza, J., Castro, P.F., Pinheiro, W.A. Using wavelets to classify documents. In: IEEE/WIC/ACMInternational Conference onWeb Intelligence and Intelligent Agent Technology, vol. 1, pp. 272-278 (2008)
91. Xiao, J., Zhang, Y., Jia, X., Li, T. Measuring similarity of interests for clustering webusers. In: ADC 2001: Proceedings of the 12th Australasian database conference, Washington, DC, USA, pp. 107-114. IEEE Computer Society, Los Alamitos (2001)
92. Zhou, Y., Leung, H., Winoto, P. Mnav: A markov model-based web site navigability measure. IEEE Trans. Softw. Eng. 33(12), 869-890 (2007)
93. Zukerman, I., Albrecht, D.W., Nicholson, A.E. Predicting users requests on the In: UM 1999: Proceedings of the seventh international conference on User modeling, Secaucus, NJ, USA, pp. 275-284. Springer-Verlag New York, Inc. (1999)