FeedMe: a collaborative alert filtering system

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. Categories and Subject Descriptors H.5.3 [Group and Organization Interfaces]: Collaborative computing, Computer-supported cooperative work, Evaluation/methodology. G.3 [Probability and Statistics]: Experimental design, Probabilistic algorithms General Terms Algorithms, Experimentation, Human Factors. Keywords Alert filtering, collaborative filtering, attention ...

• Type: Conference
• Year: 2006
• Where: CSCW
• Authors: Shilad Sen, Werner Geyer, Michael J. Muller, Marty Moore, Beth Brownholtz, Eric Wilcox, David R. Millen
• Keywords: Alert Filtering, CSCW 2006, General Alert Management, Rule-based Alert Filtering, Social Sciences