%0 Book Section %1 Mobasher07p90 %A Mobasher, Bamshad %B The Adaptive Web: Methods and Strategies of Web Personalization %C Berlin, Heidelberg %D 2007 %E Brusilovsky, Peter %E Kobsa, Alfred %E Nejdl, Wolfgang %I Springer %K data-mining personalisation personalization web-personalisation web-personalization %P 90-135 %T Data Mining for Web Personalization %U http://dx.doi.org/10.1007/978-3-540-72079-9_3 %V 4321 %X 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. %& 3 %@ 978-3-540-72078-2

@incollection{Mobasher07p90, 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.}, added-at = {2009-03-23T02:33:45.000+0100}, address = {Berlin, Heidelberg}, author = {Mobasher, Bamshad}, biburl = {https://www.bibsonomy.org/bibtex/26fee4ba718181dc1d09ccc02a704661d/pizzato}, booktitle = {The Adaptive Web: Methods and Strategies of Web Personalization}, chapter = 3, crossref = {BrusilovskyKobsaNejdl2007}, description = {Survey paper about data mining techniques for web personalisation}, editor = {Brusilovsky, Peter and Kobsa, Alfred and Nejdl, Wolfgang}, file = {SpringerLink:2007/Mobasher07p90.pdf:PDF}, interhash = {c79bd03eb83876ffa6eb9a642e7d4347}, intrahash = {6fee4ba718181dc1d09ccc02a704661d}, isbn = {978-3-540-72078-2}, keywords = {data-mining personalisation personalization web-personalisation web-personalization}, owner = {flint}, pages = {90-135}, publisher = {Springer}, series = {Lecture Notes in Computer Science}, timestamp = {2009-03-23T02:33:45.000+0100}, title = {Data Mining for Web Personalization}, url = {http://dx.doi.org/10.1007/978-3-540-72079-9_3}, volume = 4321, year = 2007 }