## 4 Experiments
### Discussion and Results
The second one is an effective recommendation list where several recommendations (i.e., 904, 1007, 1275, 1642, and 722) hit the user preference. The third one is also a typical case in the dataset where the contextual check-ins consist of the duplicated POIs (i.e., 209), which is difficult for the model to provide appropriate recommendations. The last case shows that the proposed approach may generate POIs users have visited (i.e., 1477). In other words, the diversity of recommendations should be further improved. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**Model**} \\ \cline{2-5}  & **MTPR-NoGeo** & **MTPR-NoGen** & **MTPR** \\ \hline \multirow{4}{*}{Precision} & Top5 & 0.071(0.048) & 0.120(0.035) & **0.153(0.001)** \\  & Top10 & 0.050(0.034) & 0.083(0.024) & **0.106(0.001)** \\  & Top15 & 0.040(0.027) & 0.065(0.018) & **0.082(0.000)** \\  & Top20 & 0.034(0.022) & 0.054(0.015) & **0.068(0.000)** \\ \hline \multirow{4}{*}{Recall} & Top5 & 0.058(0.039) & 0.098(0.029) & **0.125(0.001)** \\  & Top10 & 0.081(0.055) & 0.136(0.039) & **0.172(0.001)** \\ \cline{1-1}  & Top15 & 0.097(0.065) & 0.160(0.044) & **0.201(0.001)** \\ \cline{1-1}  & Top20 & 0.110(0.071) & 0.177(0.048) & **0.222(0.001)** \\ \hline \multirow{4}{*}{F1-score} & Top5 & 0.064(0.043) & 0.108(0.031) & **0.138(0.001)** \\  & Top10 & 0.062(0.042) & 0.103(0.029) & **0.131(0.001)** \\ \cline{1-1}  & Top15 & 0.057(0.038) & 0.093(0.026) & **0.117(0.001)** \\ \cline{1-1}  & Top20 & 0.051(0.033) & 0.083(0.023) & **0.104(0.000)** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The performance of MTPR using different components on Gowalla. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Metric**} & \multicolumn{4}{c}{**Model**} \\ \cline{2-5}  & **MTPR-NoGeo** & **MTPR-NoGen** & **MTPR** \\ \hline \multirow{4}{*}{Precision} & Top5 & 0.152(0.088) & 0.219(0.011) & **0.232(0.003)** \\  & Top10 & 0.113(0.060) & 0.157(0.006) & **0.166(0.001)** \\  & Top15 & 0.092(0.045) & 0.126(0.004) & **0.132(0.001)** \\  & Top20 & 0.079(0.036) & 0.106(0.003) & **0.111(0.001)** \\ \hline \multirow{4}{*}{Recall} & Top5 & 0.105(0.061) & 0.152(0.008) & **0.161(0.002)** \\  & Top10 & 0.156(0.083) & 0.217(0.009) & **0.229(0.002)** \\ \cline{1-1}  & Top15 & 0.192(0.094) & 0.261(0.009) & **0.274(0.002)** \\ \cline{1-1}  & Top20 & 0.219(0.100) & 0.292(0.009) & **0.307(0.003)** \\ \hline \multirow{4}{*}{F1-score} & Top5 & 0.124(0.072) & 0.179(0.009) & **0.190(0.002)** \\ \cline{1-1}  & Top10 & 0.131(0.069) & 0.182(0.007) & **0.192(0.002)** \\ \cline{1-1}  & Top15 & 0.125(0.061) & 0.169(0.006) & **0.178(0.002)** \\ \cline{1-1}  & Top20 & 0.116(0.053) & 0.155(0.005) & **0.163(0.001)** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The performance of MTPR using different components on Foursquare. To reveal the overall performance of our proposed approach, Tables 6 and 7 describe the performance of baselines and MTPR on the two real-world datasets. Observed from the experimental results, MTPR outperforms other baselines on both datasets, which significantly improves the Top5 recommendation. In detail, the GAN-based model Geo-ALM outperforms other baselines that shows that the structure of generative adversarial networks is capable of improving the performance of POI recommendations. In addition, the performance of PRME is fluctuating with the increasing length of recommendation list (i.e., TopN recommendations), which means that PRME cannot capture the user preference exactly but promotes the diversity of recommendations. Compared to the performance of MTPR-NoGeo and MTPR-NoGen in Tables 3 and 4 with the performance of baselines considering the geographical influence in Tables 6 and 7, both MTPR-NoGeo and MTPR-NoGen outperform the baselines which further demonstrates that the structure of geographical LSTM and the mechanism of adversarial learning are capable of improving the performance of POI recommendations.