Infinite Mixtures of Markov Chains

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Authors

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.
Original languageEnglish
Title of host publicationNew 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
EditorsAnnalisa Appice, Corrado Loglisci, Giuseppe Manco, Elio Masciari
Number of pages15
Place of PublicationCham
PublisherSpringer
Publication date2018
Pages167-181
ISBN (print)978-3-319-78679-7
ISBN (electronic)978-3-319-78680-3
DOIs
Publication statusPublished - 2018

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