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The Adaptive Web pp 90–135Cite as

Data Mining for Web Personalization

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4321))

Abstract

In this chapter we present an overview of Web personalization process viewed as an application of data mining requiring support for all the phases of a typical data mining cycle. These phases include data collection and pre-processing, pattern discovery and evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This view of the personalization process provides added flexibility in leveraging multiple data sources and in effectively using the discovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activities and techniques used at different stages of this cycle, including the preprocessing and integration of data from multiple sources, as well as pattern discovery techniques that are typically applied to this data. We consider a number of classes of data mining algorithms used particularly for Web personalization, including techniques based on clustering, association rule discovery, sequential pattern mining, Markov models, and probabilistic mixture and hidden (latent) variable models. Finally, we discuss hybrid data mining frameworks that leverage data from a variety of channels to provide more effective personalization solutions.

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  1. Center for Web Intelligence, School of Computer Science, Telecommunication, and Information Systems, DePaul University, Chicago, Illinois, USA

    Bamshad Mobasher

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Peter Brusilovsky Alfred Kobsa Wolfgang Nejdl

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Mobasher, B. (2007). Data Mining for Web Personalization. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds) The Adaptive Web. Lecture Notes in Computer Science, vol 4321. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72079-9_3

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