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KDD
2002
ACM

Relational Markov models and their application to adaptive web navigation

11 years 3 months ago
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. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications--data mining; I.2.6 [Artificial Intelligence]: Learning--induction; I.5.1 [Pattern Recognition]: Models--statistical Keyword...
Corin R. Anderson, Pedro Domingos, Daniel S. Weld
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2002
Where KDD
Authors Corin R. Anderson, Pedro Domingos, Daniel S. Weld
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