Modeling Multitasking Users

This paper describes an algorithm to cluster and segment sequences of low-level user actions into sequences of distinct high-level user tasks. The algorithm uses text contained in interface windows as evidence of the state of user–computer interaction. Window text is summarized using latent semantic indexing (LSI). Hierarchical models are built using expectation–maximization to represent users as macro models. User actions for each task are modeled with a micro model based on a Gaussian mixture model to represent the LSI space. The algorithm’s performance is demonstrated in a test of web-browsing behavior, which also demonstrates the value of the temporal constraint provided by the macro model.