With the ever expanding mobile device ecosystem, mobile users face a vast and constantly growing application pool. At the same time, in our daily life, waiting occurs regularly at different places such as shopping centers, where mobile applications become the de facto means to consume the time periods. In this paper, we propose a novel application recommendation system that utilizes human activity information at different places, to better match the applications with the characteristics of the users current contexts. Specifically, we design a place/application matching model and present two application list recommending algorithms with bounded approximation ratio. We also implement the recommendation system on real mobile phones and conduct field studies to show its feasibility. Our experimental and simulation results show that the proposed schemes can achieve satisfactory results.