Relational Markov Models and their Application to Adaptive Web Navigation

DMCA

Relational Markov Models and their Application to Adaptive Web Navigation (2002)

Cached

Download Links

Other Repositories/Bibliography

DBLP

by Corin R. Anderson , Pedro Domingos , Daniel S. Weld

Citations:

89 - 8 self

BibTeX

@MISC{Anderson02relationalmarkov,
    author = {Corin R. Anderson and Pedro Domingos and Daniel S. Weld},
    title = {Relational Markov Models and their Application to Adaptive Web Navigation},
    year = {2002}
}

OpenURL

Abstract

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.

Keyphrases

relational markov model adaptive web navigation different set sparse data good prediction alternative method different type web site user cross product previously-unvisited part web site heterogeneous state space present experiment proteus architecture markov model academic web site effective learning

DMCA