## 2 Related Work
### Personalized Location Recommendations  
Personalized location recommendations aim to recommend locations that users have not visited but may be interested in, such as natural attractions, shopping malls, etc. In order to improve recommendation performances, it is important to effectively dig out user-location latent associations. Many researchers have made contributions on this task, aiming to explore the implicit representations of user interests and location characteristics from their interactions. There are several strands of classical models for location recommendations. The first strand of model are random models, which can capture macroscopic features mathematically, but they are not accurate, such as Markov chains [28] and Bayesian personalized ranking models [29; 30], support vector machines [31]. The second strand of models are collaborative filtering models based on machine learning, which develop fast for their abilities in obtaining interactive features [5; 6; 32; 33; 34]. For example, location recommendation frameworks GeoMF and GeoMF++ introduce the geographic features into matrix factorization, which obtains implicit geographic embeddings to improve location recommendations [5; 6]. Nowadays, with the continuous expansion of data scale, deep learning-based models become the third strand of models used in the location recommendations, enhancing the representation ability of entities and hence improving their performances. [PERSON] et al. [35] use LSTM to learn the long-term interests of users' travel behavior, and provide users with personalized travel location recommendations. [PERSON] et al. [36] propose a location recommendation model based on federated learning. It trains two neural networks to realize location recommendations at the same time, by fully exploring users' implicit interests in location-visited sequences in the short term and long term. Based on GAN (generative adversarial network), [PERSON] et al. [37] propose an adversarial location recommendation model, which contains two antibodies to obtain user embeddings to recommend proper locations. Nevertheless, the aforementioned methods are still limited to obtaining optimal recommendation results because they cannot use explicit connections between entities, i.e., the similarity between users and the adjacency between locations. Recently, graph-based models are widely applied in recommender systems for their great abilities in aggregating high-order features and capturing implicit characteristics [10; 11]. Merging the idea of neural network and the representation ability of graph structure, graph neural network (GNN) is a typical application. Among various kinds of GNNs, graph convolutional neural network [38] (GCN) well outperforms the others due to its efficient integration on user-item interactions by graph convolution, which enhances the feature representation capability of recommender model. There are many research works applying GNNs into location recommendations, proving that it can works better in the recommending task due to its strong information aggregation and representation ability. [PERSON] et al. [39] propose neural graph collaborative filtering (NGCF), which uses GCN to obtain decomposed embeddings of users and items, and improves recommending results compared with traditional matrix factorization. In [40], [PERSON] et al. propose a subgraph-based graph embedding method, SgWalk, to capture contextual relationships based on user subgraph. [PERSON] et al. [41] propose Venue2 Vec model, which incorporates temporal-spatial context and semantic information into fine-grained location prediction of users. [PERSON] et al. [42] propose a location recommend framework with hybrid graph convolutional networks. Compared with collaborative filtering-based and other deep learning-based recommendation methods, GNNs can well learn the representations of users and items. However, these work ignore the node attributes and do not solve the data sparsity problem.