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We describe the use of a recommender system to enable continuous knowledge acquisition and individualized tutoring of application software across an organization. Installing such systems will result in the capture of evolving expertise and in organization-wide learning (OWL). We present the results of a year-long naturalistic inquiry into an application’s usage patterns, based on logging users’ actions. We analyze the data to develop user models, individualized expert models, and instructional indicators. We show how this information is used to recommend learning tips to users.