%0 Journal Article %1 keyhere %A Mobasher, Bamshad %D 2007 %J The Adaptive Web %K recommender-systems %P 90--135 %T Data Mining for Web Personalization %U http://dx.doi.org/10.1007/978-3-540-72079-9_3 %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 discoveryand evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This viewof the personalization process provides added flexibility in leveraging multiple data sources and in effectively using thediscovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activitiesand 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 miningalgorithms 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 discusshybrid data mining frameworks that leverage data from a variety of channels to provide more effective personalization solutions.

@article{keyhere, 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 discoveryand evaluation, and finally applying the discovered knowledge in real-time to mediate between the user and the Web. This viewof the personalization process provides added flexibility in leveraging multiple data sources and in effectively using thediscovered models in an automatic personalization system. The chapter provides a detailed discussion of a host of activitiesand 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 miningalgorithms 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 discusshybrid data mining frameworks that leverage data from a variety of channels to provide more effective personalization solutions.}, added-at = {2009-04-09T16:53:40.000+0200}, author = {Mobasher, Bamshad}, biburl = {https://www.bibsonomy.org/bibtex/272b756d5aa28705a6b6041666e8398ed/claudio.lucchese}, description = {SpringerLink - Book Chapter}, interhash = {c79bd03eb83876ffa6eb9a642e7d4347}, intrahash = {72b756d5aa28705a6b6041666e8398ed}, journal = {The Adaptive Web}, keywords = {recommender-systems}, pages = {90--135}, timestamp = {2009-04-09T16:53:40.000+0200}, title = {Data Mining for Web Personalization}, url = {http://dx.doi.org/10.1007/978-3-540-72079-9_3}, year = 2007 }