Personalisation in service-oriented systems using markov chain model and Bayesian inference

Jakub M. Tomczak, Jerzy Świa̧tek

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

In the paper a personalization method using Markov model and Bayesian inference is presented. The idea is based on the hypothesis that user's choice of a new decision is influenced by the last made decision. Thus, the user's behaviour could be described by the Markov chain model. The extracted knowledge about users' behaviour is maintained in the transition matrice as probability distribution functions. An estimation of probabilities is made by applying incremental learning algorithm which allows to cope with evolving environments (e.g. preferences). At the end an empirical study is given. The proposed approach is presented on an example of students enrolling to courses. The dataset is partially based on real-life data taken from Wrocław University of Technology and includes evolving users' behaviour.

Original languageEnglish
Title of host publicationTechnological Innovation for Sustainability - Second IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011, Proceedings
Pages91-98
Number of pages8
DOIs
Publication statusPublished - 8 Mar 2011
Externally publishedYes
Event2nd IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011 - Costa de Caparica, Portugal
Duration: 21 Feb 201123 Feb 2011

Publication series

NameIFIP Advances in Information and Communication Technology
Volume349 AICT
ISSN (Print)1868-4238

Conference

Conference2nd IFIP WG 5.5/SOCOLNET Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2011
CountryPortugal
CityCosta de Caparica
Period21/02/1123/02/11

Keywords

  • Bayesian inference
  • incremental learning
  • Markov chain
  • modeling

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