## 4 Experiments
### Discussion and Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{5}{c}{**Model**} \\ \cline{2-6}  & \multicolumn{2}{c}{**PRME**} & **POI2 Vec** & **GeoIE** & **Geo-ALM** & **MTPR** \\ \hline \multirow{4}{*}{Precision} & Top5 & 0.036(0.007) & 0.121(0.055) & 0.138(0.021) & 0.189(0.005) & **0.232**(0.003) \\  & Top10 & 0.034(0.008) & 0.123(0.043) & 0.120(0.017) & 0.152(0.002) & **0.166**(0.001) \\  & Top15 & 0.052(0.004) & 0.093(0.012) & 0.101(0.015) & 0.124(0.003) & **0.132**(0.001) \\  & Top20 & 0.064(0.003) & 0.078(0.009) & 0.088(0.005) & 0.101(0.004) & **0.111**(0.001) \\ \hline \multirow{4}{*}{Recall} & Top5 & 0.025(0.005) & 0.084(0.036) & 0.099(0.014) & 0.143(0.002) & **0.161**(0.002) \\  & Top10 & 0.047(0.004) & 0.173(0.021) & 0.165(0.007) & 0.192(0.003) & **0.229**(0.002) \\ \cline{1-1}  & Top15 & 0.108(0.003) & 0.195(0.015) & 0.203(0.006) & 0.243(0.001) & **0.274**(0.002) \\ \cline{1-1}  & Top20 & 0.180(0.005) & 0.213(0.010) & 0.224(0.003) & 0.274(0.004) & **0.307**(0.003) \\ \hline \multirow{4}{*}{F1-score} & Top5 & 0.029(0.003) & 0.099(0.086) & 0.115(0.016) & 0.163(0.003) & **0.190**(0.002) \\  & Top10 & 0.039(0.007) & 0.143(0.057) & 0.138(0.013) & 0.170(0.001) & **0.192**(0.002) \\ \cline{1-1}  & Top15 & 0.070(0.005) & 0.125(0.025) & 0.134(0.008) & 0.164(0.005) & **0.178**(0.002) \\ \cline{1-1}  & Top20 & 0.094(0.004) & 0.114(0.014) & 0.126(0.007) & 0.148(0.002) & **0.163**(0.001) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The performance of baselines and MTPR on Foursquare. \begin{table}
\begin{tabular}{l l} \hline \hline
**Contextual Check-in Sequence** & **Real Check-Ins** & **Top10 Recommendations** \\ \hline
2072, 1194, 1118 & 169, 289, 462, 486, 628, 759, 767, 839, 896, 954, 986, 1005, 1076, 1194, 1273, 1312, 1453 & 1630, 427, 276, 1621, 896, 538, 1308, 903, 1918, 1329 \\  & 1194, 1273, 1312, 1453 & 1308, 904, 1007, 1275, 83, 1226, 1642, 1447, 1641, 722, 1880 \\  & **1275**, 1448, 1498, 1571, 1574, 1610, 1642, 1654, 1747, 1971 & 1630, 427, 276, 1621, 896, 538, 1308, 903, 1918, 1329 \\  & 119, 265, 352, 473, 489, 722, 726, 904, 1007, 1120, 1132, 1154, 1273, 1447, 1641, 722, 1880 \\  & **1275**, 1448, 1498, 1571, 1574, 1610, 1642, 1654, 1747, 1971 & 1642, 1654, 1747, 1971 \\ \hline
209, 209, 209 & 144, 183, 209, 360, 362, 373, 706, 925, 933, 1058, 1219, 1416, 1432, 1551, 1566, 1629, 1677, 1758, 1947, 1971, 1981 & 1237, 899, 1180, 1807, 1017, 1758, 561, 1810, 1097, 209 \\  & 1551, 1566, 1629, 1677, 1758, 1947, 1971, 1981 & 197, 209 \\ \hline
889, **1477**, 1345 & 42, 264, 628, 701, 722, 777, 784, 889, 931, 976, 1325, 1477, 1921 & 1477, 690, 805, 1210, 333, 722, 1306, 1395, 784, 427 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The comparison between the generated samples (i.e., recommendations) and the real check-ins at Top10. However, there still exist some limitations and shortcomings which should be issued to improve the performance of POI recommendations further. First, the setup of geographical influence (i.e., coordinate LSTM) is straightforward and easily causes the problem of overfitting. A smarter strategy should be proposed to fit the path of changing coordinate for considering the geographical influence. Second, the training samples (i.e., real and fake) contain the instances that have the same contextual sequential POIs but are followed by different check-ins where these samples will confuse the models during the training process. It is challenging to relieve the influence caused by the conflicting training samples. Third, the structures of generator and discriminator are similar, which is not beneficial to adversarial learning. The significant difference between generators and discriminators may further improve the performance of the adversarial setup.