Analysis of user behaviour as time series
HCI'92 Proceedings of the conference on People and computers VII
Link prediction and path analysis using Markov chains
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Plan-based interfaces: keeping track of user tasks and acting to cooperate
Proceedings of the 7th international conference on Intelligent user interfaces
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
CTTE: support for developing and analyzing task models for interactive system design
IEEE Transactions on Software Engineering
ConcurTaskTrees: A Diagrammatic Notation for Specifying Task Models
INTERACT '97 Proceedings of the IFIP TC13 Interantional Conference on Human-Computer Interaction
An Adaptive User Interface Based On Personalized Learning
IEEE Intelligent Systems
Adaptive interfaces and agents
The human-computer interaction handbook
Probabilistic Finite-State Machines-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
TAMODIA '05 Proceedings of the 4th international workshop on Task models and diagrams
Learning User Profile from Traces
SAINT-W '05 Proceedings of the 2005 Symposium on Applications and the Internet Workshops
Automatic Evaluation Tool for Multimodal Dialogue Systems
PIT '08 Proceedings of the 4th IEEE tutorial and research workshop on Perception and Interactive Technologies for Speech-Based Systems: Perception in Multimodal Dialogue Systems
A framework for adapting interactive systems to user behavior
Journal of Ambient Intelligence and Smart Environments
The development of intuitive knowledge classifier and the modeling of domain dependent data
Knowledge-Based Systems
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The description of the user-system interaction plays a crucial role in adaptive interactive systems, since the adaptations depend on this description. User actions in interactive systems can be described as a sequence of events, which are created by input through input devices as well as by the system as reactions to these inputs. An interactive system can observe these events and thus extract information about the user's behavior. This paper presents a two-step approach for describing user behavior from sequences of basic events. First, user actions are recognized in the sequence of interaction events by means of previously trained probabilistic automata. Second, a task model describes the higher-level user activity as a hierarchical composition of these actions. Different kinds of adaptive support can be derived from this description of user behavior, such as recommending next interaction steps to the user.