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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Human-Computer Interaction
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Proceedings of the 2007 international ACM conference on Supporting group work
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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As the number of alerts generated by collaborative applications grows, users receive more unwanted alerts. FeedMe is a general alert management system based on XML feed protocols such as RSS and ATOM. In addition to traditional rule-based alert filtering, FeedMe uses techniques from machine-learning to infer alert preferences based on user feedback. In this paper, we present and evaluate a new collaborative naïve Bayes filtering algorithm. Using FeedMe, we collected alert ratings from 33 users over 29 days. We used the data to design and verify the accuracy of the filtering algorithm and provide insights into alert prediction.