Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

Bibtex Metadata Paper


Mark Girolami, Ata Kabán


To provide a compact generative representation of the sequential activ- ity of a number of individuals within a group there is a tradeoff between the definition of individual specific and global models. This paper pro- poses a linear-time distributed model for finite state symbolic sequences representing traces of individual user activity by making the assump- tion that heterogeneous user behavior may be ‘explained’ by a relatively small number of common structurally simple behavioral patterns which may interleave randomly in a user-specific proportion. The results of an empirical study on three different sources of user traces indicates that this modelling approach provides an efficient representation scheme, re- flected by improved prediction performance as well as providing low- complexity and intuitively interpretable representations.