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

Published 9 December 2003

Mark Girolami, Ata Kabán

Citations35

TL;DR

A linear-time distributed model for finite state symbolic sequences representing traces of individual user activity by making the assumption 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 is proposed.

Abstract

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. 1

Keywords

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