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.
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Notes
- 1.
We focus on first-order dependencies but the approach is easily generalized to higher-order models; notation is quickly getting messy though.
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Acknowledgements
This research has been funded in parts by the German Science Foundation DFG under grant GRK/1907 and by the German Federal Ministry of Education and Science BMBF under grant QQM/01LSA1503C.
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Reubold, J., Boubekki, A., Strufe, T., Brefeld, U. (2018). Infinite Mixtures of Markov Chains. In: Appice, A., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2017. Lecture Notes in Computer Science(), vol 10785. Springer, Cham. https://doi.org/10.1007/978-3-319-78680-3_12
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