Search

With the increasing popularity of mobile devices, a large amount of user check-in point of interests (POIs) data have been accumulated. The information of user check-in makes the recommendation of the next POI become a hot issue in recent years. The accuracy of the next POI recommendation is restricted by two aspects: On the one hand, the sparsity of check-in data. At present, most researchers alleviate data sparsity problem to a certain extent by introducing geographic correlation or friend evaluation information on POIs. However, not all POIs have strong geographic correlation, and only a small number of users comment on POIs where they have check-ins. On the other hand, deep learning based check-in sequence training has the problem of gradient disappearing. In order to solve these problems, this paper proposes a user??s next POI recommendation model which integrates the influence of regions and friends. Firstly, the region information of POIs is integrated into the sequence of POIs. Sequentially, this paper uses the neural network model with residual connection to embed the sequence, to avoid gradient vanishing and improve the convergence of the model. Finally, this model integrates the information of POIs visited by friends for recommendation, to improve the accuracy of POI recommendation. Experimental results show that the proposed model is more accurate than other existing models.

Cross-network user identification aims to identify the accounts owned by the same user across multiple networks, which is significant in friend recommendation, network security and link prediction. Existing methods mainly make full use of a small set of seed users and iteratively identify the other users. However, limited by the scale of seed users, these methods can??t reach a satisfactory accuracy with low time complexity. A method of cross-network user identification using global seed and optimal local extension (GLE) is proposed. Firstly, in order to effectively solve the cold start problem, this paper proposes a global seed expansion method (GSE) to expand the seed set. Secondly, to ensure higher accuracy at a lower time cost, this paper proposes a local search range expansion method for candidate searching. Finally, experiments demonstrate that this method can significantly improve the recall and precision of user identification at a lower time cost, and effectively solves the identification problem when the scale of seed users is insufficient.