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Conference Paper: Mining of web-page visiting patterns with continuous-time markov models

TitleMining of web-page visiting patterns with continuous-time markov models
Authors
Huang, QimingYang, QiangHuang, Joshua ZhexueNg, Michael K.
KeywordsWeb mining
Sessions
Kolmogorov’s backward equations
Continuous time markov chain
Transition probability
Issue Date2004
PublisherSpringer.
Citation
8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2004), Sydney, Australia, 26-28 May 2004. In Advances in Knowledge Discovery and Data Mining:8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004: Proceedings, 2004, p. 549-558 How to Cite?
DOI: http://dx.doi.org/10.1007/978-3-540-24775-3_65
Abstract© Springer-Verlag Berlin Heidelberg 2004. This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the total number of visits of a given Web page by all incoming users at the same time. The prediction technique can be used as a component for many Web based applications. The prediction model regards a Web browsing session as a continuous-time Markov process where the transition probability matrix can be computed from Web log data using the Kolmogorov’s backward equations. The model is tested against real Web-log data where the scalability and accuracy of our method are analyzed.
Persistent Identifierhttp://hdl.handle.net/10722/276853
ISBN
9783540220640
ISSN
0302-9743
2023 SCImago Journal Rankings: 0.606
Series/Report no.Lecture Notes in Computer Science ; 3056

 

DC FieldValueLanguage
dc.contributor.authorHuang, Qiming-
dc.contributor.authorYang, Qiang-
dc.contributor.authorHuang, Joshua Zhexue-
dc.contributor.authorNg, Michael K.-
dc.date.accessioned2019-09-18T08:34:51Z-
dc.date.available2019-09-18T08:34:51Z-
dc.date.issued2004-
dc.identifier.citation8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2004), Sydney, Australia, 26-28 May 2004. In Advances in Knowledge Discovery and Data Mining:8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004: Proceedings, 2004, p. 549-558-
dc.identifier.isbn9783540220640-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/276853-
dc.description.abstract© Springer-Verlag Berlin Heidelberg 2004. This paper presents a new prediction model for predicting when an online customer leaves a current page and which next Web page the customer will visit. The model can forecast the total number of visits of a given Web page by all incoming users at the same time. The prediction technique can be used as a component for many Web based applications. The prediction model regards a Web browsing session as a continuous-time Markov process where the transition probability matrix can be computed from Web log data using the Kolmogorov’s backward equations. The model is tested against real Web-log data where the scalability and accuracy of our method are analyzed.-
dc.languageeng-
dc.publisherSpringer.-
dc.relation.ispartofAdvances in Knowledge Discovery and Data Mining:8th Pacific-Asia Conference, PAKDD 2004, Sydney, Australia, May 26-28, 2004: Proceedings-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 3056-
dc.subjectWeb mining-
dc.subjectSessions-
dc.subjectKolmogorov’s backward equations-
dc.subjectContinuous time markov chain-
dc.subjectTransition probability-
dc.titleMining of web-page visiting patterns with continuous-time markov models-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-540-24775-3_65-
dc.identifier.scopuseid_2-s2.0-7444220174-
dc.identifier.spage549-
dc.identifier.epage558-
dc.identifier.eissn1611-3349-
dc.publisher.placeBerlin-
dc.identifier.issnl0302-9743-

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