@inproceedings{26436b6f59b84206b34680b4fba001f4,
title = "Learning profiles based on hierarchical hidden markov model",
abstract = "This paper presents a method for automatically constructing a sophisticated user/process profile from traces of user/process behavior. User profile is encoded by means of a Hierarchical Hidden Markov Model (HHMM). The HHMM is a well formalized tool suitable to model complex patterns in long temporal or spatial sequences. The method described here is based on a recent algorithm, which is able to synthesize the HHMM structure from a set of logs of the user activity. The algorithm follows a bottom-up strategy, in which elementary facts in the sequences (motives) are progressively grouped, thus building the abstraction hierarchy of a HHMM, layer after layer. The method is firstly evaluated on artificial data. Then a user identification task, from real traces, is considered. A preliminary experimentation with several different users produced encouraging results.",
author = "Ugo Galassi and Attilio Giordana and Lorenza Saitta and Maco Botta",
year = "2005",
doi = "10.1007/11425274\_5",
language = "English",
isbn = "3540258787",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "47--55",
booktitle = "Foundations of Intelligent Systems - 15th International Symposium, ISMIS 2005, Proceedings",
address = "Germany",
note = "15th International Symposium on Methodologies for Intelligent Systems, ISMIS 2005 ; Conference date: 25-05-2005 Through 28-05-2005",
}