# Heterogeneous Graph Structure Learning for Next Point-of-Interest Recommendation
## 2 Literature Review
### Next Point-of-Interest Recommendation  
The next point-of-interest models are mainly divided into two categories: the first category is based on sequential models, and the second category is based on graph neural network models. Among the graph-based models, some papers choose to use self-supervised learning to assist the recommendation process.  
Sequence-based models primarily view a user's check-in history as a sequence, learning from this sequence to predict future check-ins. Early models often utilized Markov Chains (MCs) and Matrix Factorization (MF) techniques, such as FPMC [1], which combined MC and MF by decomposing the users' transition matrix to capture long-term preferences. However, with the advancement of deep learning, many researchers have turned to Recurrent Neural Networks (RNNs) and their variants, which offer significant advantages in sequential modeling. For example, ST-RNN [6] integrates spatiotemporal context into the RNN-based neural network layer. In LSPL [19] and PLSPL [20], LSTM is used to capture short-term preferences. STGN [21] employs two time gates and two distance gates to capture spatiotemporal features in both long-term and short-term preferences. STAN [22] uses an attention mechanism to capture relations between non-adjacent check-ins, while also incorporating spatiotemporal information.  
Sequence-based models can only focus on the users' local context and cannot capture their behaviors from a global perspective. In contrast, graph neural networks can model the connections between users and POIs from a broader perspective. For example, GE [10] has constructed several relation graphs between users and POIs, resulting in representation vectors for both users and POIs. HMT-GRN [23] has created a user-region matrix to reduce the sparsity of the interaction matrix between users and POIs. GETNext [15] believes that recommending the next point-of-interest is not just a sequential recommendation task. In addition to use Transformer-based sequential modeling, it also constructs a trajectory graph reflecting the order of users' check-ins. This graph helps learn the probability of users moving from the current POI to any other POIs, which is then added to the sequence recommendation results. The STHGCN [16] model uses hypergraphs to capture high-order information, linking user history trajectories with the check-in patterns of different users.  
Self-supervised learning has emerged as a promising method in deep learning. In next POI recommendation, this method can help capture users' latent preferences or the potential relations between POIs, improving the performance of personalized recommendation. For example, GSBPL [24] considers the sequential visits in the graph and uses two augmentation strategies, edged masking and bridging, as well as edge inversion, to create different viewsfor graph contrastive learning. SLS-REC [25] uses a node drop strategy to generate views for the contrastive learning of users' long-term and short-term interests. LSPSL [26] focuses on data sparsity and dynamic user preference. The authors design two objectives to enhance sequence representations by exploiting the spatio-temporal context. CLLP [27] considers users' preference diversity and data sparsity and propose to remove noise from POI representations by leveraging the collaborative signals from other users. These papers have validated the self-supervised-learning-based method in POI recommendation.