Experience with a learning personal assistant
Communications of the ACM
Learning in the presence of concept drift and hidden contexts
Machine Learning
Tracking Context Changes through Meta-Learning
Machine Learning - Special issue on multistrategy learning
Machine Learning - Special issue on context sensitivity and concept drift
A hybrid user model for news story classification
UM '99 Proceedings of the seventh international conference on User modeling
Selecting Examples for Partial Memory Learning
Machine Learning
Using Decision Trees for Agent Modeling: Improving Prediction Performance
User Modeling and User-Adapted Interaction
User Modeling and User-Adapted Interaction
Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
Machine Learning
Incremental Learning from Noisy Data
Machine Learning
Personalised hypermedia presentation techniques for improving online customer relationships
The Knowledge Engineering Review
Temporal evolution and local patterns
LPD'04 Proceedings of the 2004 international conference on Local Pattern Detection
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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The paper presents an algorithm for learning drifting and recurring user interests. The algorithm uses a prior-learning level to find out the current context. After that, searches into past observations for episodes that are relevant to the current context, 'remembers' them and 'forgets' the irrelevant ones. Finally, the algorithm learns only from the selected relevant examples. The experiments conducted with a data set about calendar scheduling recommendations show that the presented algorithm improves significantly the predictive accuracy.