C4.5: programs for machine learning
C4.5: programs for machine learning
Horting hatches an egg: a new graph-theoretic approach to collaborative filtering
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
The Development and Prospect of Personalized TV Program Recommendation Systems
MSE '02 Proceedings of the Fourth IEEE International Symposium on Multimedia Software Engineering
Learning and reasoning about interruption
Proceedings of the 5th international conference on Multimodal interfaces
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
OSGS—A Personalized Online Store for E-Commerce Environments
Information Retrieval
If not now, when?: the effects of interruption at different moments within task execution
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Controlling interruptions: awareness displays and social motivation for coordination
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
Effects of intelligent notification management on users and their tasks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Machine Learning: Modeling Data Locally and Globally
Machine Learning: Modeling Data Locally and Globally
Modeling user perception of interaction opportunities in collaborative human-computer settings
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A collaborative recommender system based on user association clusters
WISE'05 Proceedings of the 6th international conference on Web Information Systems Engineering
Determining the value of information for collaborative multi-agent planning
Autonomous Agents and Multi-Agent Systems
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In collaborative systems involving a user and an agent working together on a joint task it may be important to share information in order to determine the appropriate course of action. However, communication between agents and users can create costly user interruptions. One of the most important issue concerning the initiation of information sharing in collaborative systems is the ability to accurately estimate the cost and benefit arising from those interruptions. While cost estimation of interruptions has been previously investigated, these works assumed either a large amount of information was available about each user, or only a small number of states needed consideration. This paper presents a novel synthesis between Collaborative Filtering methods with classification algorithms tools to create a fast learning algorithm, MICU. MICU exploits the similarities between users in order to learn from known users to new but similar users and therefore requires less information on each user in compare to other methods. Experimental results indicate the algorithm significantly improves system performance even with a small amount of data on each user.