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Latent community discovery through enterprise user search query modeling

Enterprise computer networks are filled with users performing a variety of tasks, ranging from business-critical tasks to personal interest browsing. Due to this multi-modal distribution of behaviors, it is non-trivial to automatically discern which behaviors are business-relevant and which are not. Additionally, it is difficult to infer communities of interest within the enterprise, even given an organizational mapping. In this work, we present a two-step framework for classifying user behavior within an enterprise in a data-driven way. As a first step, we use a latent topic model on active search queries to identify types of behaviors and topics of interest associated with a given user. We then leverage the information about user's assigned role within the organization to extract relevant topics which are most reflective of self-organizing communities of interest. We demonstrate that our framework is able to identify rich communities of interest that are better representations of how users interact and assemble in an enterprise setting.