Standard
Infinite Mixtures of Markov Chains. / Reubold, Jan
; Boubekki, Ahcène; Strufe, Thorsten et al.
New Frontiers in Mining Complex Patterns: 6th International Workshop, NFMCP 2017 : held in conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017 : revised selected papers. Hrsg. / Annalisa Appice; Corrado Loglisci; Giuseppe Manco; Elio Masciari. Cham: Springer, 2018. S. 167-181 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10785 LNAI).
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
Harvard
Reubold, J
, Boubekki, A, Strufe, T
& Brefeld, U 2018,
Infinite Mixtures of Markov Chains. in A Appice, C Loglisci, G Manco & E Masciari (Hrsg.),
New Frontiers in Mining Complex Patterns: 6th International Workshop, NFMCP 2017 : held in conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017 : revised selected papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Bd. 10785 LNAI, Springer, Cham, S. 167-181.
https://doi.org/10.1007/978-3-319-78680-3_12APA
Reubold, J.
, Boubekki, A., Strufe, T.
, & Brefeld, U. (2018).
Infinite Mixtures of Markov Chains. In A. Appice, C. Loglisci, G. Manco, & E. Masciari (Hrsg.),
New Frontiers in Mining Complex Patterns: 6th International Workshop, NFMCP 2017 : held in conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017 : revised selected papers (S. 167-181). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Band 10785 LNAI). Springer.
https://doi.org/10.1007/978-3-319-78680-3_12Vancouver
Reubold J
, Boubekki A, Strufe T
, Brefeld U.
Infinite Mixtures of Markov Chains. in Appice A, Loglisci C, Manco G, Masciari E, Hrsg., New Frontiers in Mining Complex Patterns: 6th International Workshop, NFMCP 2017 : held in conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017 : revised selected papers. Cham: Springer. 2018. S. 167-181. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-78680-3_12
Bibtex
@inbook{7d5b1f3d79ee45d4bfd044c4a46c047c,
title = "Infinite Mixtures of Markov Chains",
abstract = "Facilitating a satisfying user experience requires a detailed understanding of user behavior and intentions. The key is to leverage observations of activities, usually the clicks performed on Web pages. A common approach is to transform user sessions into Markov chains and analyze them using mixture models. However, model selection and interpretability of the results are often limiting factors. As a remedy, we present a Bayesian nonparametric approach to group user sessions and devise behavioral patterns. Empirical results on a social network and an electronic text book show that our approach reliably identifies underlying behavioral patterns and proves more robust than baseline competitors.",
keywords = "Business informatics",
author = "Jan Reubold and Ahc{\`e}ne Boubekki and Thorsten Strufe and Ulf Brefeld",
year = "2018",
doi = "10.1007/978-3-319-78680-3_12",
language = "English",
isbn = "978-3-319-78679-7",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "167--181",
editor = "Annalisa Appice and Corrado Loglisci and Giuseppe Manco and Elio Masciari",
booktitle = "New Frontiers in Mining Complex Patterns",
address = "Germany",
}
RIS
TY - CHAP
T1 - Infinite Mixtures of Markov Chains
AU - Reubold, Jan
AU - Boubekki, Ahcène
AU - Strufe, Thorsten
AU - Brefeld, Ulf
PY - 2018
Y1 - 2018
N2 - Facilitating a satisfying user experience requires a detailed understanding of user behavior and intentions. The key is to leverage observations of activities, usually the clicks performed on Web pages. A common approach is to transform user sessions into Markov chains and analyze them using mixture models. However, model selection and interpretability of the results are often limiting factors. As a remedy, we present a Bayesian nonparametric approach to group user sessions and devise behavioral patterns. Empirical results on a social network and an electronic text book show that our approach reliably identifies underlying behavioral patterns and proves more robust than baseline competitors.
AB - Facilitating a satisfying user experience requires a detailed understanding of user behavior and intentions. The key is to leverage observations of activities, usually the clicks performed on Web pages. A common approach is to transform user sessions into Markov chains and analyze them using mixture models. However, model selection and interpretability of the results are often limiting factors. As a remedy, we present a Bayesian nonparametric approach to group user sessions and devise behavioral patterns. Empirical results on a social network and an electronic text book show that our approach reliably identifies underlying behavioral patterns and proves more robust than baseline competitors.
KW - Business informatics
UR - http://www.scopus.com/inward/record.url?scp=85045204646&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-78680-3_12
DO - 10.1007/978-3-319-78680-3_12
M3 - Article in conference proceedings
SN - 978-3-319-78679-7
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 167
EP - 181
BT - New Frontiers in Mining Complex Patterns
A2 - Appice, Annalisa
A2 - Loglisci, Corrado
A2 - Manco, Giuseppe
A2 - Masciari, Elio
PB - Springer
CY - Cham
ER -
