Abstract
Personalization services pose new challenges to interest mining on Portal. Capturing the surfing behaviors of users implicitly and mining interest navigation patterns are the top demanding tasks. Based on the analysis of mapping the personalization interest behaviors on Portal, a novel Portalindependent mechanism of interest elicitation with privacy protection is proposed, which implements both the implicit extraction of diverse behaviors and their semantic analysis. Moreover, we present a hidden Markov model extension with personalization interest description of Portal to form interest navigation patterns for different users. Then experiments have been carried out in order to validate the proposed approaches.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Albanese, M., et al.: Web personalization based on static information and dynamic behavior. In: Proceedings of the ACM WIDM’04, pp. 80–87. ACM Press, New York (2004)
Kim, D.-H., et al.: A clickstream–based collaborative filtering personalization model: Towards a better performance. In: Proceedings of the ACM WIDM’04, pp. 88–94. ACM Press, New York (2004)
Lancieri, L., Durand, N.: Internet user behavior: compared study of the access traces and application to the discovery of communities. IEEE Transactions on System, Man and Cybernetics-Part A: Systems and Humans 36(1) (2006)
Oikonomopoulou, D., Rigou, M., Sirmakessis, S., et al.: Full-Coverage Web prediction based on Web usage mining and site topology. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, ACM Press, New York (2004)
Chen, M., LaPaugh, A., Singh, J.P.: Categorizing information objects from user access patterns. In: Proceedings of the ACM CIKM’02, pp. 365–372. ACM Press, New York (2002)
Velásquez, J., Yasuda, H., Aoki, T.: Combining the Web content and usage mining to understand the visitor behavior in a web site. In: Proceedings of the 3rd IEEE International Conference on Data Mining, IEEE Computer Society Press, Los Alamitos (2003)
Wang, S., Gao, W., Jin-Tao, L., et al.: Mining interest navigation patterns based on Hidden Markov model. Chinese Journal of Computers 24(2), 152–157 (2001)
Wu, J., Xiong, Z.: A Portal-oriented personalized recommendation using meta-recommender engine. In: Proceedings of the International Conference on Artificial Intelligence, China, pp. 570–576 (2006)
Zhou, B., Hui, S.C., Fong, A.C.M.: Discovering and visualizing Temporal-based Web access behavior. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence, ACM Press, New York (2005)
Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proceedings of the 25th ACM SIGIR, ACM Press, New York (2002)
Weitzner, D.J., et al.: Transparent accountable data mining: new strategies for privacy protection. Computer Science and Artificial Intelligence Laboratory Technical Report (2006), http://www.csail.mit.edu
W3C.org: http://www.w3.org/2000/10/swap/
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer Berlin Heidelberg
About this paper
Cite this paper
Wu, J., Zhang, P., Xiong, Z., Sheng, H. (2007). Mining Personalization Interest and Navigation Patterns on Portal. In: Zhou, ZH., Li, H., Yang, Q. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2007. Lecture Notes in Computer Science(), vol 4426. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71701-0_106
Download citation
DOI: https://doi.org/10.1007/978-3-540-71701-0_106
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71700-3
Online ISBN: 978-3-540-71701-0
eBook Packages: Computer ScienceComputer Science (R0)