Recently, some physical dynamics, including heat conduction and mass diffusion, have found their applications in personalized recommendation. These kinds of nature-inspired approaches have been demonstrated to be both highly efficient and of low computational complexity. However, most of them rely only on the connections between users and objects, but does not take into consideration the sequence of user-object collecting activities. In this paper, the temporal information of users' objectcollecting activities is adopted to measure the user-user similarity. we propose a list-wise diffusion-based recommender algorithm, which assigns the user-user similarity as the weight to the links of user-object bipartite network. Experimental results on two benchmark datasets show that our proposed approach can not only enhance the accuracy, but also largely provide more diverse recommendations.
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