## 1 Introduction  
The evolution of mobile phones and wearable devices facilitates the development of location-based social networks (LBSNs), which bridges the gap between the virtual cyberspace and the physical world. LBSNs, such as Foursquare and Gowalla, provide detailed information on massive POIs in our physical world, and users can share their check-in experience based on reviews (e.g., texts and photographs) in the virtual cyberspace. To improve the user satisfaction, the point-of-interest (POI) recommendation is proposed to capture the user preferences based on sufficient information (e.g., check-ins and ratings) and push potential desired POIs to individuals. Therefore, the research of POI recommender systems becomes increasingly popular, and researchers make many efforts on the effectiveness and efficiency of POI recommendation [1; 2; 3]. However, the characteristics of location-based social networks also bring significant challenges to current techniques of recommender systems. First, different from the explicit feedback (i.e., ratings) in traditional recommender systems (i.e., books and movies), the check-in records in LBSNs are the implicit feedback that reflect the user preferences. In other words, high ratings mean definite user preferences to a specific item; however, the amount of check-ins is not relevant to user preferences. Second, although check-in records are much more than ratings due to the characteristics of user behaviors in LBSNs, check-in data is still extremely sparse, which may cause a cold-start problem and make it difficult for models to capture user preferences. Third, it is necessary to simultaneously consider the user preference and geographical influence in POI recommendations, which should be a multi-objective optimization to balancing the trade-off between the user preference and the physical distance. Therefore, there still exist some exciting and challenging tasks that should be resolved in POI recommender systems. To overcome the challenges mentioned above, many researchers have made lots of efforts on POI recommendations based on innovative techniques. [PERSON] et al. proposed a strategy for relaxing the count of check-ins to several labels distinguishing user preferences and built a classification model for recommending POIs to individuals [4]. Although the problem of implicit feedback is relieved based on the relaxation of preference representation, the extreme situation (e.g., a favorite POI has been visited only once) cannot be considered. To this end, they also proposed an attention-based recurrent neural network for concentrating on the sequential check-in behavior to further relieve the problem of implicit feedback instead of focusing on the count of check-ins on a specific POI [5]. However, due to the sparsity of check-ins, the proposed model in [5] is hard to recommend for POIs that the user has never been to. In addition, there exists much deep learning research applied to the mechanism of word embedding for representing each POI using a continuous vector simultaneously considering the sequential check-ins and the characteristics of POI to improve the performance of POI recommendation [6; 7]. However, the sparsity of check-in records also makes these deep learning models challenging for capturing the relevance among massive POIs and users.Therefore, it is significant to relieve the influence of sparse check-in data on the effectiveness of POI recommendations. To this end, in this paper, we propose a multi-task leaning based POI recommendation simultaneously considering the temporal check-ins and the geographical locations. First, the proposed framework exploits a generative adversarial network (GAN) that constructs an adversarial game between a generator (G) and a discriminator (D). Based on the mechanism of adversarial learning, G aims to fit the real data and learn the actual distribution of datasets while D tries to distinguish whether the incoming sample is real or fake (i.e., generated by G).