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In this paper, we study the non-monotone DR-submodular function maximization over integer lattice. Functions over integer lattice have been defined submodular property that is similar to submodularity of set functions. DR-submodular is a further extended submodular concept for functions over the integer lattice, which captures the diminishing return property. Such functions find many applications in machine learning, social networks, wireless networks, etc. The techniques for submodular set function maximization can be applied to DR-submodular function maximization, e.g., the double greedy algorithm has a $1/2$-approximation ratio, whose running time is $O(nB)$, where $n$ is the size of the ground set, $B$ is the integer bound of a coordinate. In our study, we design a $1/2$-approximate binary search double greedy algorithm, and we prove that its time complexity is $O(n\log B)$, which significantly improves the running time. Specifically, we consider its application to the profit maximization problem in social networks with a bipartite model, the goal of this problem is to maximize the net profit gained from a product promoting activity, which is the difference of the influence gain and the promoting cost. We prove that the objective function is DR-submodular over integer lattice. We apply binary search double greedy algorithm to this problem and verify the effectiveness.