document: Relational Markov models and their application to adaptive web navigation Relational Markov models ( RMMs ) are a generalization of Markov models where states can be of different types , with each type described by a different set of variables . The domain of each variable can be hierarchically structured , and shrinkage is carried out over the cross product of these hierarchies . RMMs make effective learning possible in domains with very large and heterogeneous state spaces , given only sparse data . We apply them to modeling the behavior of web site users , improving prediction in our PROTEUS architecture for personalizing web sites . We present experiments on an e-commerce and an academic web site showing that RMMs are substantially more accurate than alternative methods , and make good predictions even when applied to previously-unvisited parts of the site .
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