Titlebook: UM99 User Modeling; Proceedings of the S Judy Kay Conference proceedings 1999 Springer-Verlag Wien 1999 Dialog Management.Hypermedia Naviga

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https://doi.org/10.1007/978-3-7091-2490-1Dialog Management; Hypermedia Navigation; Information Retrieval; Interface Adaption; Machine Learning; ar

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UM99 User Modeling978-3-7091-2490-1Series ISSN 0254-1971 Series E-ISSN 2309-3706

一窩小鳥

Building User and Expert Models by Long-Term Observation of Application Usagenal topics. The new user model is based on observing the individual’s behavior in a natural environment over a long period of time, while the new expert model is based on pooling the knowledge of numerous individuals. Individualized instructional topics are selected by comparing an individual’s knowledge to the pooled knowledge of her peers.

Binge-Drinking

Abnormal

Using Plan Recognition in Human-Computer Collaborationrchical plans, and the possibility of asking for clarification. We demonstrate how the addition of our plan recognition algorithm to an implemented collaborative system reduces the amount of communication required from the user.

Charlatan

User Lenses — Achieving 100% Precision on Frequently Asked Questionse performance on training data while not degrading test data performance, and that larger lenses result in nearly perfect performance on the training set. The lens provides a mechanism for automatically capturing long-term, user-specific information about an improved representation scheme for document vectors.

ALB

The Application of User Modeling Techniques to Reason about the Human Contribution to Major Accidentper argues that user modeling techniques can help to improve our understanding of operator interaction in the lead-up to accidents and incidents. This argument is illustrated by a “near miss” that occurred when two Air Traffic Controllers failed to coordinate the arrival and departure of aircraft at Heathrow Airport on the 27. August 1997.

饑荒

Patterns of Search: Analyzing and Modeling Web Query Refinementian networks that predict search behavior, with a focus on the progression of queries over time. We review a methodology for abstracting and tagging user queries. After presenting key statistics on query length, query frequency, and informational goals, we describe user models that capture the dynamics of query refinement.