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Feature-rich software applications offer users hundreds of commands, yet most people use only a very small fraction of the available command set. Command recommenders aim to increase awareness of an application's capabilities by generating personalized recommendations for new commands. A primary distinguishing characteristic of these recommenders concerns the manner in which they determine command relevance. Social approaches do so by analyzing community usage logs, whereas, task-baseddoi:10.1145/2856767.2856814 dblp:conf/iui/WiebeGB16 fatcat:4shs5redijgq5cerj5yr4wkxba