Users’ recognition in web using web mining techniques

Abstract: The rapid growth of the web and the lack of structure or an integrated schema create various issues to access the information for users. All users’ access on web information are saved in the related server log files. The circumstance of using these files is implemented as a resource for finding some patterns of user & apos; s behavior. Web mining is a subset of data mining and it means the mining of the related data from WWW, which is categorized into three parts including web content mining, web structure mining and web usage mining, based on the part of data, which is mined. It seems necessary to have a technique, which is capable of learning the users’ interests and based on the interests, which could filter the unrelated interests automatically or it could offer the related information to the user in reasonable amount of time. The web usage mining makes a profile from users to recognize them and it has direct relationship to web personalizing. The primary objective of personalizing systems is to prepare the thing, which is required by users, without asking them explicitly. In the other way, formal models prepare the possibility of system’s behavior modeling. The Petri and queue nets as some samples of these models can analyze the user & apos; s behavior in web. The primary objective of this paper is to present a colored Petri net to model the user & apos; s interactions for offering a list of pages recommendation to them in web. Estimating the user & apos; s behavior is implemented in some cases like offering the proper pages to continue the browse in web, ecommerce and targeted advertising. The preliminary results indicate that the proposed method is able to improve the accuracy criterion 8.3% rather static method.

How to cite this paper

 Ghazanfaripoor, H., Harounabadi, A & Sabaghmolahoseini, A. (2013). Users’ recognition in web using web mining techniques.Management Science Letters , 3(6), 1707-1712.

Refrences

Adomavicius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A
survey of the state-of-the-art and possible extensions. Knowledge and Data Engineering, IEEE
Transactions on, 17(6), 734-749.

Breeding, M. (2005). Analyzing Web Server Logs to Improve a Site & apos; s Usage. The Systems
Librarian. Computers in Libraries, 25(9), 26-28.

Forsati, R., & Meybodi, M.R. (2009). Algorithmic based on structure pages and user’s usage
information for recommending web pages. The 2nd Iranian data mining conference.

Heylighen, F., & Bollen, J. (2002). Hebbian algorithms for a digital library recommendation system.

In Parallel Processing Workshops, 2002. Proceedings. International Conference on (pp. 439-446).
IEEE.

Jensen, K. (1994). An introduction to the theoretical aspects of coloured petri nets (pp. 230-272).
Springer Berlin Heidelberg.

Mobasher, B., Dai, H., Luo, T., & Nakagawa, M. (2001). Effective personalization based on
association rule discovery from web usage data. In Proceedings of the 3rd international workshop
on Web information and data management (pp. 9-15). ACM.

Phatak, D. S., & Mulvaney, R. (2002). Clustering for personalized mobile web usage. In Fuzzy
Systems, 2002. FUZZ-IEEE & apos; 02. Proceedings of the 2002 IEEE International Conference on (Vol.
1, pp. 705-710). IEEE.

Selvan, M. P., Sekar, A. C., & Dharshini, A. P. (2012). Survey on web page ranking algorithms.
International Journal of Computer Applications, 41(19), 1-7.

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Management Science Letters

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