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

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. © 2011 IFIP International Federation for Information Processing.