## 2 Related Work
Compared to the current POI recommendation approaches, our proposed model aims to address the sparsity of check-in records using the mechanism of adversarial learning and taking the geographical influence into account for improving the effectiveness of user and POI embeddings. In addition, the user's preference is fluctuating according to the current situation (e.g., the current location and the previous check-in POI). Therefore, the mechanism of attention is utilized to adjust the embedding based on the complex contextual information. Generative Adversarial Networks: Due to the effectiveness of addressing various problems and improving the performance of models, generative adversarial networks (GAN) have been attracting a lot of research attention in recent years. The principle of GAN is to jointly train a generator and a discriminator based on a game setting using the adversarial learning, and one or both of the generators and the discriminator will be leveraged to complete proposed tasks [17]. The utilization of GAN in the task of POI recommendation is limited, and Geo-ALM is a simple try to generate critical negative samples using GAN [10]. However, GAN is a simple form of adversarial learning where the framework of GAN is evolving with the development of machine learning models. GAN was proposed to address the problems in the image processing domain due to the advantage of handling continuous data [17]. The goal of recommender systems is to push the items which hit the preference of users where the items are discrete data. In other words, the gradient between discrete data cannot be calculated. Therefore, traditional GAN cannot be leveraged in the domain of recommender systems. To this end, [PERSON] et al. proposed a GAN-based information retrieval model named IRGAN for transforming the traditional gradient into the policy gradient based reinforcement learning [18]. Although IRGAN is capable of generating recommendations (i.e., discrete data), the contradiction between real and fake data will confuse the discriminator and restrict the performance of the generator. To alleviate this problem, [PERSON] et al. proposed a generic collaborative filtering framework based on GAN named CFGAN, which discarded the softmax layer and transformed the output back to the continuous form [19]. On the one hand, it can calculate the gradient of the continuous data simply. On the other hand, GAN is designed to generate continuous data, which means GAN's performance should be improved. In addition, the goal of the generator is to learn the distribution of real data based on the Kullback-Leibler divergence, which may cause the gradient vanishing and the gradient exploding. To address this issue, [PERSON] et al. proposed the Wasserstein distance, which describes the difference between distributions better than the Kullback-Leibler divergence [20]. [PERSON] et al. proposed a deep adversarial substructured learning framework to learning the representation of mobile user profiling where the proposed framework maintained two components preserved the entire graph and the sub graph of users [21]. [PERSON] et al. designed a spatial embedding strategy to unifying the inter- and intra-region autocorrelations using a collective graph-regularized dual adversarial learning framework [22]. In addition, [PERSON] et al. proposed a zero-centered gradient penalty for improving the stability of GANs and the generalization of the discriminator by pushing it toward the optimal discriminator [23]. Attention: The mechanism of attention, which was called memory networks at the beginning, was proposed to address problems in the domain of natural language processing [24]. For example, when we deal with the task of reading comprehension, we are more concerned about the key sentences, even words to our questions than the irrelevant ones. However, the recurrent neural networks (i.e., long-short term memory networks) consider that the latest words are more significant than any other, and the convolutional neural networks treat each word fairly.