Skip to Main content Skip to Navigation
Journal articles

WebUser: mining unexpected web usage

Haoyuan Li 1 Anne Laurent 1 Pascal Poncelet 1
1 TATOO - Fouille de données environnementales
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : Web usage mining has been much concentrated on the discovery of relevant user behaviours from Web access record data. In this paper, we present WebUser, an approach to discover unexpected usage in Web access log. We present a belief-driven method for extracting unexpected Web usage sequences, where the belief system consists of a temporal relation and semantics constrained sequence rules acquired with respect to prior knowledge. Our experiments show the effectiveness and usefulness of the proposed approach. Further, discovered rules of unexpected Web usage can be used for Web content personalisation and recommendation, site structure optimisation, and critical event prediction.
Document type :
Journal articles
Complete list of metadata

Cited literature [44 references]  Display  Hide  Download
Contributor : Pascal Poncelet <>
Submitted on : Friday, March 22, 2019 - 3:59:25 PM
Last modification on : Thursday, June 3, 2021 - 3:32:11 PM
Long-term archiving on: : Sunday, June 23, 2019 - 4:01:57 PM


Files produced by the author(s)




Haoyuan Li, Anne Laurent, Pascal Poncelet. WebUser: mining unexpected web usage. International Journal of Business Intelligence and Data Mining, Inderscience, 2011, 6 (1), pp.90-111. ⟨10.1504/IJBIDM.2011.038276⟩. ⟨lirmm-00798139⟩



Record views


Files downloads