A learning interface agent for scheduling meetings
IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
Agents that reduce work and information overload
Communications of the ACM
Experience with a learning personal assistant
Communications of the ACM
Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Active preference learning for personalized calendar scheduling assistance
Proceedings of the 10th international conference on Intelligent user interfaces
Deploying a personalized time management agent
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Entropy-Driven online active learning for interactive calendar management
Proceedings of the 12th international conference on Intelligent user interfaces
Formal concept analysis as mathematical theory of concepts and concept hierarchies
Formal Concept Analysis
Design and evaluation of a command recommendation system for software applications
ACM Transactions on Computer-Human Interaction (TOCHI)
PTIME: Personalized assistance for calendaring
ACM Transactions on Intelligent Systems and Technology (TIST)
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In this paper, we present SmartCal, a calendar assistant that suggests appointment attributes, such as time, day, duration, etc., given any combination of initial user input attributes. SmartCal uses closed pattern mining to discover patterns in past appointment data in order to represent user preferences and adapt to changing user preferences over time. The SmartCal interface is designed to be minimally intrusive: users are free to choose or ignore suggestions, which are dynamically updated as users enter new information. The user model as a collection of patterns is intuitive and transparent: users can view and edit existing patterns or create new patterns based on existing appointments. SmartCal was evaluated in a user study with four users over a four week period. The user study shows that pattern mining makes appointment creation more efficient and users regarded the appointment suggestion feature favourably.