Leveraging Users for Efficient Interruption Management in Agent-User Systems

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.

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