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- W2794290800 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." @default.
- W2794290800 created "2018-03-29" @default.
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- W2794290800 date "2018-01-01" @default.
- W2794290800 modified "2023-09-28" @default.
- W2794290800 title "Infinite Mixtures of Markov Chains" @default.
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- W2794290800 doi "https://doi.org/10.1007/978-3-319-78680-3_12" @default.
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