## 5 Conclusions  
In this paper, we propose a multi-task learning based POI recommendation approach simultaneously considering the sequential check-in records and the geographical influence. The experimental results show that both the module of geographical LSTM and the setup of adversarial learning are capable of improving the performance of POI recommendations on the real-world datasets (i.e., Foursquare and Gowalla). In addition, the performance of the proposed method outperforms the baselines, which also take the geographical influence into account, especially at the Top5 recommendations, which demonstrates the effectiveness of MTPR to capture the user preference.  
In the future, there still exists some work for which it is worth making an effort. First, besides the geographical influence, the category of POI is also a significant factor for the next visited POI in the sequential check-in records. It would be better to simultaneously consider the geographical and the category influence for capturing the user preference. Second, deep learning-based GAN training is time-consuming and hard to use in practical scenarios. An effective online learning approach would make these deep learning-based methods accessible in recommender systems. Third, the TopN recommendation is a traditional strategy for providing delicate items based on the relevance between users and items. However, if the user preference cannot be captured precisely, the ranking list is not meaningful anymore. Therefore, it is necessary to propose a kind of recommendation strategy considering the changing user preference.  
**Author Contributions:** Conceptualization, B.X., Y.B. and J.Y.; methodology, B.X.; validation, Y.B. and J.Y.; formal analysis, Q.L.; investigation, L.X.; resources, Y.B. and J.Y.; data curation, Y.B. and J.Y.; writing--original draft preparation, B.X.; writing--review and editing, B.X.; visualization, B.X.; supervision, B.X.; project administration, B.X.; funding acquisition, B.X. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{6}{c}{**Model**} \\ \cline{2-7}  & & **PRME** & **POI2 Vec** & **GeoE** & **Geo-ALM** & **MTPR** \\ \hline \multirow{4}{*}{Precision} & Top5 & 0.053(0.006) & 0.098(0.065) & 0.115(0.018) & 0.123(0.002) & **0.153(0.001)** \\  & Top10 & 0.044(0.007) & 0.082(0.049) & 0.090(0.020) & 0.099(0.001) & **0.106(0.001)** \\  & Top15 & 0.030(0.005) & 0.060(0.015) & 0.066(0.016) & 0.079(0.003) & **0.082(0.000)** \\  & Top20 & 0.400(0.002) & 0.053(0.008) & 0.062(0.006) & 0.065(0.001) & **0.068(0.000)** \\ \hline \multirow{4}{*}{Recall} & Top5 & 0.045(0.004) & 0.082(0.034) & 0.098(0.012) & 0.111(0.002) & **0.125(0.001)** \\  & Top10 & 0.073(0.004) & 0.140(0.025) & 0.138(0.009) & 0.148(0.002) & **0.172(0.001)** \\  & Top15 & 0.079(0.002) & 0.143(0.013) & 0.161(0.005) & 0.173(0.004) & **0.201(0.001)** \\  & Top20 & 0.122(0.003) & 0.170(0.012) & 0.215(0.002) & 0.220(0.001) & **0.222(0.001)** \\ \hline \multirow{4}{*}{F1-score} & Top5 & 0.048(0.003) & 0.089(0.080) & 0.105(0.015) & 0.117(0.001) & **0.138(0.001)** \\  & Top10 & 0.054(0.005) & 0.103(0.053) & 0.108(0.015) & 0.119(0.001) & **0.131(0.001)** \\ \cline{1-1}  & Top15 & 0.043(0.004) & 0.084(0.022) & 0.093(0.006) & 0.108(0.001) & **0.117(0.001)** \\ \cline{1-1}  & Top20 & 0.186(0.004) & 0.080(0.013) & 0.096(0.004) & 0.100(0.002) & **0.104(0.000)** \\ \hline \hline \end{tabular}
\end{table}
Table 7: The performance of baselines and MTPR on Gowalla.  
**Funding:** This work was supported by the National Natural Science Foundation of China under Grant No. 61802205, the Natural Science Foundation of Jiangsu Higher Education Institutions of China under Grant No. 18 KJB520037, and the research funds of Nanjing University of Posts and Telecommunications under Grant No. NY218116.  
**Conflicts of Interest:** The authors declare no conflict of interest.