Building Automatic Web Customer Profiling Service

Bassel Alkhatib(1*), Ammar Alnahhas(2), Hadi Ezaldeen(3)

(1) Syrian Virtual University, Syrian Arab Republic
(2) Syrian Virtual University, Syrian Arab Republic
(3) Syrian Virtual University, Syrian Arab Republic
(*) Corresponding author

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As the web has spread widely in the past years, it is now very important to personalize and customize websites so that we can improve user experience. Customer profiling is one of the most important methods to add personalization and customization to websites as it can capture the properties and interests of the customer in order to use them later to service him properly. In this paper, we present a generalized automatic customer profiling model that can suite most websites. Our model is based on probability theory; it captures user behavior slots in order to predict his profile. These probabilistic slots values are used to generate the customer adequate services. We show how to incorporate this model in a web service so that any website developer can use this service to add automatic user profiling to his website.
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Customer Profiling, Automatic Web Site Personalization, Web Service

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Palgrave Macmillan: Customer profiling in e-commerce: Methodological aspects and challenges. The Journal of Database Marketing, Volume 9, Number 2, 1 January 2002 , pp. 170-184(15).

Mulvenna, M., Anand, S.S., Buchner, A.G.: Personalization on the net using web mining. Communication of ACM 43

Intelligent Techniques for Web Personalization. B. Mobasher and S.S. Anand. Springer-Verlag Berlin Heidelberg 2005.

Automatic Personalization Based on Web Usage Mining. B. Mobasher, R. Cooly, and J. Srivastava. COMMUNICATIONS OF THE ACM.

Analysis of Recommendation Algorithms for E-Commerce. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. ACM.

Knowledge Discovery from Users Web-Page Navigation. C. Shahabi, et al. IEEE.

Category-Based Web Personalization System. Ching-Cheng Lee, Wei Xu. IEEE.

Improving New User Recommendations with Rule-based Induction on Cold User Data. An-Te Nguyen.

Yoda: An Accurate and Scalable Web-based Recommendation System. C. Shahabi, F. Banaei-Kashani, Y. Chen, D. McLeod. In the Proceedings of The Sixth International Conference on Cooperative Information Systems. Trento, Italy. (September 2001).

Improving User Profiles for E-Commerce by Genetic Algorithms. Y. Chen, C. Shahabi. Integrated Media Systems and Computer Science Department. USC, LA.

Mladenic, D.: Personal web watcher: Implementation and design. Technical Report IJSDP- 7472, Department of Intelligent Systems, J. Stefan Institute, Slovenia.

Krulwich, B., Burkey, C.: Learning user information interests through extraction of semantically significant phrases. In: Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access.

Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35.

Herlocker, J. L.; Konstan, J. A.; Terveen, L. G. &Riedl, J. T. “Evaluating collaborative filtering recommender systems”, ACM Trans. Inf. Syst. 22 (1): 5–53 (January 2004). Recommendations, Item-to-Item Collaborative Filtering. G. Linden, B. Smith, and J. York. IEEE INTERNET COMPUTING (February 2003).

Ungar, L., Foster, D.P.: Clustering methods for collaborative filtering. In: Proceedings of the Workshop on Recommendation Systems.

David Chappell, Chappell & Associates: Introducing Windows CommunicationFoundation, Microsoft publication, July 2013.


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