Abstract
Web applications such as personalization and recommendation have raised the concerns of people because they are crucial to improve customer services, particularly for E-commerce Websites. Understanding customer preferences and requirements in time is a premise to optimize these Web services. In this paper, a new data model for Web data is introduced to analyze user behavior. The merit of the cube model is that it not only aggregates user access information but also takes the Web structure information into account. Based on the model, we propose some solutions to intelligently discover interesting user access patterns for Website optimization, Web personalization and recommendation. We used the Web usage data from a sports Website in China to evaluate the effectiveness of the model. The results show that this integrated data model is effective and efficient to apply into practical Web applications.
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© 2004 Springer-Verlag Berlin Heidelberg
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Wu, E.H., Ng, M.K., Huang, J.Z. (2004). An Efficient Multidimensional Data Model for Web Usage Mining. In: Yu, J.X., Lin, X., Lu, H., Zhang, Y. (eds) Advanced Web Technologies and Applications. APWeb 2004. Lecture Notes in Computer Science, vol 3007. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24655-8_40
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DOI: https://doi.org/10.1007/978-3-540-24655-8_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-21371-0
Online ISBN: 978-3-540-24655-8
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