# Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation
## 4 Results and Discussion
### POI Recommendation Based on Human Mobility Pattern  
We selected the travel data and check-in data of New York City in May and June 2012 for the experiment. The user check-in data came from the Foursquare dataset publicly available on Kaggle, which contains 227,428 user check-in data. We aggregated the information on the check-in time according to the hour, and obtained 1464 time slices of the check-in data. Arranging the check-in data in chronological order, we took the first 70% of the data as the training set and removed the points of interest that users have checked in to from the remaining 30% data to obtain the test set.  
Comparative experimental baseline models include the deep factorization machine (DeepFM) [50], the representational multi-layer perceptron (EmbeddingMLP), the deep-learning-based collaborative filtering model (NeuralCF) [51], the Wide&Deep model, and the Twin-tower model [52]. In the comparison experiment on the POI recommendation model, this paper used the accuracy rate (ACC) and two AUC values (ROC and PR) as the experimental indicators.  
The experimental results are shown in Table 2. It can be seen that the HMRec algorithm proposed in this paper achieved results that exceed all baseline models in terms of accuracy and receiver operating characteristic curve (ROC), which proves that the urban spatio-temporal attribute of human mobility patterns plays a certain role in POI recommendation. It is worth noting that the performance of HMRec on the precision-recall curve (PR) is slightly lower than that of the DeepFM algorithm because the DeepFM model does not incorporate prior information on human mobility patterns and thus has a higher degree  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Indicator** & **GCN-CNN-Trans** & **N2V-MLP-Trans** & **ANRL-MLP-Trans** & **GCN-MLP-GRU** & **HMPE** \\ \hline
**RMSE** & 0.1693 & 0.1517 & 0.1807 & 0.1473 & 0.1466 \\
**MAPE** & 34.70\% & 28.39\% & 44.71\% & 26.06\% & 25.65\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of urban traffic congestion prediction.  
of fitting to positive samples. Among all the baseline models, the performance of the Twin-tower model is relatively poor, which may be due to the fact that in the case of sparse samples, using a deep network for both users and points of interest can easily produce over-fitting results, resulting in the test set. The generalization ability is relatively poor. In contrast, NeuralCF achieves better results than the Twin-tower model due to its simplicity.  
By visualizing the ROC and PR curves (as shown in Figure 3), it can be seen that HMRec, Wide&Deep, and DeepFM are relatively close in terms of ROC curves. The curve of EmbeddingMLP has a steeper change in the middle, indicating that it does not perform well when predicting scores around 0.5. From the PR curve, it can be seen that the performance of HMRec, Wide&Deep, and DeepFM is still relatively stable, and Wide&Deep is not as good as HMRec and DeepFM after the recall rate increases. The performance of NeuralCF is very unstable, and the curve has large fluctuations. We can see that the HMRec represented by the red line achieves the best results.  
However, it must be acknowledged that our proposed method may have some drawbacks. The acquisition and analysis of crowd movement patterns may raise concerns regarding personal privacy and data protection. Although crowd movement patterns can provide useful information about user behavior, the sparsity of data may impact the effectiveness of the model as users typically visit only a small fraction of the overall set of locations. This can result in inaccurate predictions for certain locations or user behaviors.