## 2 Related Work  
POI Recommendation: The difference between the problem of POI recommendation and the recommendation of other types of items is that for the recommendation of POIs the geographical location of the POIs becomes an essential part of the user preferences. Traditional recommender systems (i.e., book recommendation) can provide every item that hits the user preference if the item is not out of stock. However, when a user is looking for a place for dinner after watching a film, it is ridiculous if a POI recommender system pushes a restaurant thousands of miles away from the user ignoring the geographical influence. Therefore, simultaneously considering the geographical influence and preference becomes a popular area that has attracted a lot of research attention. [PERSON] et al. proposed a personalized ranking metric embedding approach to integrating sequential check-ins, preference, and geographical influence. Different from the self-learning POI embedding, the authors utilized the metric embedding to representing each POI in a K-dimensional latent space and leveraged the Euclidean distance to describing the potential transition within the following information [8]. In the following research of [PERSON] et al., they proposed a latent representation model POI2 Vec inspired by Word2 Vec [6]. Instead of initializing embeddings using the Gaussian distribution, POI2 Vec initializes the POI vectors incorporating the geographical influence. Different from their previous work [8], they utilized the relationship between the distance and visiting probability to further represent the POI embeddings. A joint model is then proposed to combine the geographical influence with the user preference and predict the potential visitors for a specific POI. However, in these works, the geographical influence is considered as the physical distance among POIs. [PERSON] et al. redefined the geographical influence as the geo-influence of POI, the susceptibility of POI, and the physical distance, where the geo-influence describes the capacity of POI and the susceptibility describes the propensity of POI [9]. In other words, they do not only consider the physical distance between POIs as the geographical influence but also consider the influence of visiting users. Although the research of geographical influence improves the performance of current POI recommendations, the sparsity of check-ins is also a big challenge where negative sampling can alleviate this issue effectively. [PERSON] et al. proposed geographical information based adversarial learning model named Geo-ALM, which utilized a game setting to generate a few but critical negative samples (i.e., POIs that users have never been to) for improving the effectiveness of POI recommendation [10]. [PERSON] et al. focused on the geographical influence and proposed a region embedding strategy considering the distance and mobility connectivity using the multi-view learning model [11]. [PERSON] et al. combined a nonlocal network for the long-term preference and a geo-dilated recurrent neural network for the short-term preference to address the bias of current techniques in the long-term or short-term preference [12]. [PERSON] et al. proposed a general adversarial learning based POI recommender system which was inspired by IRGAN using the technique of reinforcement learning to update the generator (i.e., recommender) [13]. [PERSON] et al. was the first attempt to utilize the content-aware embedding to address the problem of representing the characteristics of POIs where the check-in layer and the content layer are combined to capture the embedding of POIs [14]. [PERSON] et al. proposed a generic deep learning architecture to optimizing the collaborative filtering recommender system which is also an effective solution in the task of the POI recommendation [15]. [PERSON] et al. also proposed a generic recommender system using the cognitive similarity-based collaborative filtering technique which is capable of addressing the general task of POI recommendation [16].