<XML><RECORDS><RECORD><REFERENCE_TYPE>3</REFERENCE_TYPE><REFNUM>7419</REFNUM><AUTHORS><AUTHOR>Girolami,M.</AUTHOR><AUTHOR>Kaban,A.</AUTHOR></AUTHORS><YEAR>2004</YEAR><TITLE>Simplicial Mixtures of Markov Chains: Distributed Modelling of Dynamic User Profiles</TITLE><PLACE_PUBLISHED>Advances in Neural Information Processing Systems 16, eds Sebastian Thrun and Lawrence Saul and Bernhard Scholkopf, MIT Press</PLACE_PUBLISHED><PUBLISHER>MIT Press</PUBLISHER><PAGES>9 - 16</PAGES><LABEL>Girolami:2004:7419</LABEL><ABSTRACT>To provide a compact generative representation of the sequential activity of a number of individuals within a group there is a tradeoff between the definition of individual specific and global models. This paper proposes 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 structurally simple common 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, reflected by improved prediction performance as well as providing low-complexity and intuitively interpretable representations.</ABSTRACT></RECORD></RECORDS></XML>