Integrate Influence of Regions and Friends for Next POI Recommendation

Abstract

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.

Keywords

• point of interest (poi);
• poi recommendation;
• social relationship;
• region;
• residual network