Statistical methods for speech recognition
Statistical methods for speech recognition
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
User interactions with everyday applications as context for just-in-time information access
Proceedings of the 5th international conference on Intelligent user interfaces
Margin notes: building a contextually aware associative memory
Proceedings of the 5th international conference on Intelligent user interfaces
Intermediaries personalize information streams
Communications of the ACM
The Art of Human-Computer Interface Design
The Art of Human-Computer Interface Design
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Learning and inferencing in user ontology for personalized Semantic Web search
Information Sciences: an International Journal
Activity recognition using eye-gaze movements and traditional interactions
Interacting with Computers
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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.