With the wide use of Location-Based Social Networks (LBSNs), predicting user friendship from online social relations and offline trajectory data is of great value to improve the platform service quality and user satisfaction. Existing methods mainly focus on some hand-crafted features or graph embedding models based on the user-location bipartite graph, which cannot precisely capture the latent mobility similarity for the majority users who have no explicit co-visit behaviors and also fail to balance the trade-off between social features and mobility features for friendship prediction. In this regard, we propose a dual subgraph-based pairwise graph neural network (DSGNN) for friendship prediction in LBSNs, which extracts a pairwise social subgraph and a trajectory subgraph to model the social proximity and mobility similarity, respectively. Specifically, to overcome the co-visit data sparsity, we design an entropy-based random walk to construct a location graph that captures the high-level correlation between locations. Based on this, we characterize the pairwise mobility similarity from trajectory level instead of location level, which is modeled by a graph neural network (GNN) on a labeled trajectory subgraph composed of the two trajectories of the target user pair. Besides, we also utilize another GNN to extract social proximity based on social subgraph of the target user pair. Finally, we propose a gate layer to adaptively balance the fusion of the social and mobility features for friendship prediction. We conduct extensive experiments on the real-world datasets and demonstrate the superiority of our approach which outperforms other state-of-the-art methods. In particular, the comparative experiments on the trajectory level mobility similarity further validate the effectiveness of the designed trajectory subgraph-based method which can extract predictive mobility features.

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Next location recommendation aims to mine users' historical trajectories to predict their potentially preferred locations in the next moment. Although previous studies have explored the idea of incorporating location or social contextual information for recommendation, they still suffer from several major limitations: (1) not fully considering the semantic associations between locations, (2) not considering the heterogeneity in preferences of socially linked users, (3) not fully utilizing contextual information from distinctive sources to further improve the recommendation performance. In this paper, we propose a novel multi-context-based next location recommendation model that incorporates location context, trajectory context, and social context to obtain comprehensive users' preferences while allowing for interactions between contexts. Specifically, we first develop an efficient method combining both high-order location graphs and location semantic graphs to characterize

Next location recommendation aims to mine users’ historical trajectories to predict their potentially preferred locations in the next moment. Although previous studies have explored the idea of incorporating location or social contextual information for recommendation, they still suffer from several major limitations: (1) not fully considering the semantic associations between locations, (2) not considering the heterogeneity in preferences of socially linked users, (3) not fully utilizing contextual information from distinctive sources to further improve the recommendation performance. In this paper, we propose a novel multi-context-based next location recommendation model that incorporates location context, trajectory context, and social context to obtain comprehensive users’ preferences while allowing for interactions between contexts. Specifically, we first develop an efficient method combining both high-order location graphs and location semantic graphs to characterize subtle associations between locations. Then we explore the social contextual information and introduce the location subgraph which considers heterogeneous preferences among friends. Finally, we use the LSTM and geo-dilated LSTM to capture the spatio-temporal associations between users’ trajectories and integrate various contextual information to improve model performance. Extensive experiments on three real datasets show that our model has superior results in the next location recommendation task over other baselines.