Interruptions can have a significant impact on users working to complete a task. When people are collaborating, either with other users or with systems, coordinating interruptions is an important factor in maintaining efficiency and preventing information overload. Computer systems can observe user behavior, model it, and use this to optimize the interruptions to minimize disruption. However, current techniques often require long training periods that make them unsuitable for online collaborative environments where new users frequently participate. In this paper, we present a novel synthesis between Collaborative Filtering methods and machine learning classification algorithms to create a fast learning algorithm, CRISP. CRISP exploits the similarities between users in order to apply data from known users to new users, therefore requiring less information on each person. Results from user studies indicate the algorithm significantly improves users' performances in completing the task and their perception of how long it took to complete each task.