Abstract
This paper presents a conceptual based approach for improving a Web site content. Usually Web Usage Mining (WUM) techniques study the visitors’ browsing behavior to obtain interesting knowledge. However, most of the work in the area leave behind the semantic information of web pages. We propose to combine the Concept-Based Knowledge Discovery in Text with the visitors sessions to perform the personalization task. This way, it is possible to obtain information about which are the users’ goals when browsing a web site. Moreover, it is possible to give better browsing recomendations and help managers improving the content of their Web site. We test this idea on a real Web site to show its effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Chakrabarti, S.: Data Mining for Hypertext: A Tutorial Survey. SIGKDD Explorations 1 (2000)
Chau, R., Yeh, C.-H.: Filtering multilingual web content using fuzzy logic and self-organizing maps. Neural Comput. Appl. 13(2), 140–148 (2004)
Eirinaki, M., Lampos, C., Paulakis, S., Vazirgiannis, M.: Web personalization integrating content semantics and navigational patterns. In: WIDM 2004: Proceedings of the 6th annual ACM international workshop on Web information and data management, pp. 72–79. ACM Press, New York (2004)
Loh, S., Oliveira, J.P.M.D., Gameiro, M.A.: Knowledge discovery in texts for constructing decision support systems. Applied Intelligence 18(3), 357–366 (2003)
Mobasher, B., Cooley, R., Srivastava, J.: Automatic personalization based on web usage mining. Commun. ACM 43(8), 142–151 (2000)
Nakanishi, H., Turksen, I.B., Sugeno, M.: A review and comparison of six reasoning methods. Fuzzy Sets and Systems 57(3), 257–294 (1993)
Nielsen, J.: User Interface directions for the web. Communications of ACM 42(1), 65–72 (1999)
Perkowitz, M., Etzioni, O.: Adaptive web sites. Commun. ACM 43(8), 152–158 (2000)
Ríos, S.A., Velasquez, J.D., Vera, E.S., Yasuda, H., Aoki, T.: Establishing guidelines on how to improve the web site content based on the identification of representative pages. In: IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology, September 2005, pp. 284–288. IEEE Computer Society Press, Los Alamitos (2005)
Ríos, S.A., Velasquez, J.D., Vera, E.S., Yasuda, H., Aoki, T.: Using SOFM to Improve Web Site Text Content. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3611, pp. 622–626. Springer, Heidelberg (2005)
Ríos, S.A., Velasquez, J.D., Yasuda, H., Aoki, T.: Web Site Improvements Based on Representative Pages Identification. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS (LNAI), vol. 3809, pp. 1162–1166. Springer, Heidelberg (2005)
Spiliopoulou, M., Mobasher, B., Berendt, B., Nakagawa, M.: A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis. INFORMS J. on Computing 15(2), 171–190 (2003)
Velásquez, J.D., Ríos, S.A., Bassi, A., Yasuda, H., Aoki, T.: Towards the identification of keywords in the web site text content: A methodological approach. International Journal of Web Information Systems 1(1), 11–15 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ríos, S.A., Velásquez, J.D., Yasuda, H., Aoki, T. (2006). Conceptual Classification to Improve a Web Site Content. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2006. IDEAL 2006. Lecture Notes in Computer Science, vol 4224. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11875581_104
Download citation
DOI: https://doi.org/10.1007/11875581_104
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-45485-4
Online ISBN: 978-3-540-45487-8
eBook Packages: Computer ScienceComputer Science (R0)