In order to solve the problem of not using the product heterogeneity well in recommendation algorithm, a modified mass diffusion algorithm was presented by considering the effect of the object popularity information on the user preference prediction. By introducing a tunable parameter of product popularity and simulating the mass diffusion process on the user-product bipartite network, the effect of the product popularity was quantitatively characterized. The experimental results on three empirical data sets which named MovieLens, Netflix and Last.FM show that, compared with the traditional mass diffusion method, the proposed algorithm can enhance the average ranking score by 25.6%, 10.96% and 1.2% respectively, and increase the diversity of the recommendation lists by 59.30%, 53.07% and 8.59% respectively. The proposed non-equilibrium mass diffusion algorithm can get more practical results.