Infinite Mixtures of Markov Chains
Publikation: Beiträge in Sammelwerken › Aufsätze in Konferenzbänden › Forschung › begutachtet
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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
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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 -