Bayesian clustering of discrete Markov chains for predicting Web-page accesses

by Oxana Chakoula

Sequences of users' requests to a web server can be robustly modeled as Markov chains. There is a tradeoff between the order of a model (and its number of states) and predictive accuracy. We model web sessions as a probabilistic mixture of first order Markov chains. By grouping sessions of similarly-minded users and utilizing this knowledge for prediction, we aim to improve predictive power while retaining lower model complexity.

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