It is well-known that different users navigate websites differently, being more or less inclined to browse or search and so forth. It is also very likely that the same user will exhibit different behaviors at different times- looking for a particular item one time, and browsing without a great deal of direction another. Knowing the type of behavior a user exhibits in a session would allow a website to tailor the information it displays to that behavior, and even to affect the behavior being displayed. We present a mathematical framework in which we directly try to learn a user’s mode of browsing during a given session. This framework is inspired by sequential analysis in the setting of educational testing. We demonstrate its feasibility and utility in the context of click-stream data and explore the range of models and variations that this framework makes available
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