# A Tour Recommendation System Considering Implicit and Dynamic Information
## 5 Experiment Design and Discussion
### Comparison of Recommendation Performance  
Tables 4 and 5 compare the seven methods' recommendation results on the testing datasets. Table 4 focuses on the metrics of P@K and MAP@K, while Table 5 focuses on MRR@K and NDCG@K. The numbers shown in bold present the best performance of each column in the table. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **P@1** & **P@3** & **P@5** & **P@7** & **P@9** & **MAP@1** & **MAP@3** & **MAP@5** & **MAP@7** & **MAP@9** \\ \hline RAND & 0.1113 & 0.1176 & 0.1218 & 0.1218 & 0.1218 & 0.0251 & 0.0516 & 0.0633 & 0.0650 & 0.0650 \\ \hline NEAR & 0.2798 & 0.2483 & 0.2318 & 0.2193 & 0.2193 & 0.0427 & 0.0885 & 0.1172 & 0.1387 & 0.1387 \\ \hline FPMC & 0.2645 & 0.2578 & 0.2463 & 0.2442 & 0.2442 & 0.0557 & 0.1052 & 0.1658 & 0.1714 & 0.1714 \\ \hline LSTM & 0.3000 & 0.2532 & 0.2410 & 0.2469 & 0.2469 & 0.0611 & 0.1157 & 0.1486 & 0.1584 & 0.1584 \\ \hline BiLSTM & 0.3177 & 0.2636 & 0.2519 & 0.2574 & 0.2574 & 0.0611 & 0.1160 & 0.1508 & 0.1686 & 0.1686 \\ \hline LSTMwATT & 0.3306 & 0.3022 & 0.2897 & 0.2887 & 0.2887 & **0.0616** & **0.1281** & 0.1715 & 0.1901 & 0.1901 \\ \hline TRANS & 0.2734 & 0.2669 & 0.2574 & 0.2567 & 0.2567 & 0.0562 & 0.1199 & 0.1545 & 0.1645 & 0.1645 \\ \hline This study & **0.4290** & **0.3662** & **0.3606** & **0.3400** & **0.3400** & 0.0613 & 0.1276 & **0.1767** & **0.2327** & **0.2327** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Recommendation performance comparison using P@K and MAP@K.  
RAND has the worst performance regarding the four metrics because the next POI is chosen randomly from all unvisited POIs. The second-worst performance is NEAR, which selects the nearest destination from a set of unvisited POIs while considering geographic location. FMPC is better than RAND and NEAR but still worse than the proposed method and all model-based methods of LSTM, BiLSTM, LSTMwATT, and TRANS since the user-POI matrices are very spare. Compared to the model-based methods of LSTM, BiLSTM, LSTMwATT, and TRANS, the proposed method outperforms all metrics except MAP@1 and MAP@3. The MAP@1 and MAP@3 of the proposed method are found to be slightly lower than the ones of LSTMwATT. However, when the K value is larger (5, 7, 9), the proposed method still performs better than LSTMwATT regarding MAP metric. Note that LSTMwATT is the best model-based method compared with LSTM, BiLSTM, and TRANS. It is worth noting that pure attention mechanism TRANS does not perform better than the hybrid approach of LSTM enhanced by attention (LSTMwATT). A tour recommender is typically considered good if a tourist can visit more POIs under the same time limitation. On the other hand, if the traveling time of a generated tour plan is close to the desired time limitation, the tour recommender is also considered good. Figure 5(a) shows the average number of POIs each method suggests. The average number of POIs of the proposed method is the highest (8.0), while the second is the NEAR method (7.2). The worst case appears in the FMPC method, which only suggests 3.6 POIs on average. Figure 5(b) shows the average remaining time left for each method when the time limitation is 8 h. The figure shows that the proposed method has the lowest remaining time left (0.13 h), while TRANS has the highest remaining time left (1.57). That means the proposed method can generate the tour with a total travel time of 7.87 h, which is close to the desired time limitation (8 h). Based on the two figures, we conclude that the proposed method can provide a better tour plan than the baseline methods.