Infinite Mixtures of Markov Chains

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

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. ed. / Annalisa Appice; Corrado Loglisci; Giuseppe Manco; Elio Masciari. Cham: Springer, 2018. p. 167-181 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10785 LNAI).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Reubold, J, Boubekki, A, Strufe, T & Brefeld, U 2018, Infinite Mixtures of Markov Chains. in A Appice, C Loglisci, G Manco & E Masciari (eds), 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), vol. 10785 LNAI, Springer, Cham, pp. 167-181. https://doi.org/10.1007/978-3-319-78680-3_12

APA

Reubold, J., Boubekki, A., Strufe, T., & Brefeld, U. (2018). Infinite Mixtures of Markov Chains. In A. Appice, C. Loglisci, G. Manco, & E. Masciari (Eds.), 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 (pp. 167-181). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10785 LNAI). Springer. https://doi.org/10.1007/978-3-319-78680-3_12

Vancouver

Reubold J, Boubekki A, Strufe T, Brefeld U. Infinite Mixtures of Markov Chains. In Appice A, Loglisci C, Manco G, Masciari E, editors, 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. p. 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

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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 -