Personalized recommendation with adaptive mixture of Markov models

Journal of the American Society for Information Science and Technology

Volumn 58, Issue 12, 2007, Pages 1851-1870

  Liu, Yang ^a   Huang, Xiangji ^a   An, Aijun ^a  

^a YORK UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CLUSTER ANALYSIS; DATA STRUCTURES; MARKOV PROCESSES; MATHEMATICAL MODELS; NAVIGATION; WEBSITES;

MARKOV MODELS; NAVIGATION PATTERNS; WEB-BASED KNOWLEDGE;

ADAPTIVE SYSTEMS;

EID: 35348877319     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.20631     Document Type: Article

References (28)

1. Aggarwal, C.C., Han, J., Wang, J., & Yu, P.S. (2003). A framework for clustering evolving data streams. In VLDB, pp. 81-92, Berlin, Germany: Morgan Kaufmann.
2. Agrawal, R., Imielinski, T., & Swami, A.N. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (207-216). Washington, D.C.: ACM Press.
3. Anderson, C., Domingos, P., & Weld, D. (2002). Relational Markov models and their application to adaptive. Web navigation. In Proceedings of eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 143-152). Madison, Wisconsin: ACM Press.
4. Bilmes, J. (1997). A gentle tutorial on the em-algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report, University of Berkeley, ICSI-TR-97-021.
5. Box, G.E.P., & Jenkins, G.M. (1994). Time series analysis: Forecasting and control. Upper Saddle River, NJ: Prentice Hall PTR.
6. Cadez, L.V., Heckerman, D., Meek, C., Smyth, P., & White, S. (2000). Visualization of navigation patterns on a Web site using model-based clustering. In Knowledge Discovery and Data Mining (pp. 280-284). Boston: ACM Press.
7. Fu, Y., Sandhu, K., & Shih, M.-Y. (1999). Generalization-based approach to clustering of Web usage sessions. In WEBKDD (pp. 21-38). San Diego: Springer.
8. Gunduz, S., & Ozsu, M.T. (2003). A Web page prediction model based on click-stream tree representation of user behavior. In Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 535-540). Philadelphia: ACM Press.
9. Huang, X, Peng, F., An, A., & Schuurmans, D. (2004). Dynamic Web log session identifcation with statistical language models. Journal of American Society for Information Science and Technology, Special Issue on Webometrics, 55(14), 1290-1303. John Wiley & Sons, Inc.
10. Huo, Q., Chan, C., & Lee, C.-H. (1995). Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition. IEEE
11. ransactions on Speech and Audio Processing, 3(5), 334-345. IEEE Signal Processing Society.
12. Manavoglu, E., Palov, D., & Giles, C.L. (2003). Probabilistic user behavior models. In ICDM'03: Proceedings of the Third IEEE International Conference on Data Mining (p. 203). Melbourne, Fl: IEEE Computer Society.
13. Milligan, G.W., & Cooper, M.C. (1986). A study of the comparability of external criteria for hierarchical cluster analysis. Multivariate Behavioral Research, 21, 441-458. Society of Multivariate Experimental Psychology.
14. Mobasher, B., Dai, H., Luo, T., & Nakagawa, M. (2001). Effective personalization based on association rule discovery from Web usage data. In Proceedings of the Third International Workshop on Web Information and Data Management (pp. 9-15). Arlington, VA: ACM Press.
15. Mobasher, B., Dai, H., Luo, T., Sun, Y., & Zhu, J. (2000). Integrating Web usage and content mining for more effective personalization. In EC-WEB '00: Proceedings of the First International Conference on Electronic Commerce and Web Technologies (pp. 165-176). London: Springer.
16. Padmanabhan, V.N., & Mogul, J.C. (1996). Using predictive prefetching to improve World Wide Web latency. In Proceedings of the ACM SIGCOMM Comput. Comm. Rev., 26(3): 22-36, New York: ACM Press.
17. Pirolli, P., & Pitkow, J.E. (1999). Distributions of surfers' paths through the World Wide Web: Empirical characterizations. World Wide Web, 2(1-2), 29-45. Hingham, MA, USA: Kluwer Academic Publishers.
18. Sarukkai, R.R. (2000). Link prediction and path analysis using Markov chains. In Proceedings of the Ninth International World Wide Web Conference on Computeter Networks: The International Journal of computer and Telecommunications Networking (pp. 377-386). Amsterdam, The Netherlands: North-Holland Publishing Co.
19. Schechter, S., Krishnan, M., & Smith, M.D. (1998). Using path profiles to predict http requests. In Proceedings of the Seventh International
20. onference on World Wide Web 7 (pp. 457-467). Brisbane, Australia: Elsevier Science Publishers B.V.
21. Shahabi, C., Zarkesh, A.M., Adibi, J., & Shah, V. (1997). Knowledge discovery from user's Web-page navigation. In RIDE. Washington, D.C.: IEEE Computer Society.
22. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating "word of mouth." In Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems (Vol. 1, pp. 210-217). New York, NY, USA: ACM Press/Addison-Wesley Publishing Co.
23. Smyth, P. (1996a). Clustering sequences with hidden Markov models. In NIPS (pp. 648-654). Denver, CO: MIT Press.
24. Smyth, P. (1996b). Clustering using Monte Carlo cross-validation. In Knowledge Discovery and Data Mining (pp. 126-133). Portland, OR: AAAI Press.
25. Yan, T.W, Jacobsen. M., Garcia-Molina, H., & Dayal, U. (1996). From user access patterns to dynamic hypertext linking. In Proceedings of the Fifth International World Wide Web Conference on Computer Networks and ISBN Systems (pp. 1007-1014). Amsterdam, The Netherlands: Elsevier Science Publishers.
26. Zhong, S., & Ghosh, J. (2003). A unified framework for model-based clustering. Journal of Machine Learning Research, 4, 1001-1037. Cambridge, MA: MIT Press.
27. Zukerman, I., & Albrecht, D.W. (2001). Predictive statistical models for user modeling. User Modeling and User-Adapted Interaction, 11(1-2), 5-18. Hingham, MA: Kluwer Academic Publishers.
28. Zivkovic, Z. (2004). Improved adaptive Gaussian mixture model for background subtraction. In Proceedings of the International Conference on Pattern Recognition. Washington, D.C.: IEEE Computer Society.