# Exploring Prior Knowledge from Human Mobility Patterns for POI Recommendation
## 4 Results and Discussion  
### Urban Traffic Congestion Status Assessment Task  
In this paper, in order to ensure that the spatio-temporal pattern extraction framework can learn urban spatio-temporal features and at the same time evaluate the learning effect of this feature, we design urban congestion situation prediction as a downstream task of the extraction framework. This is also a general practice for evaluating the performance of vector representations in the hidden middle layers of an encoding-decoding framework [43]. We used yellow taxi data and POI data from May and June 2012 in New York City to conduct a comparative experiment. The experiment selected the spatial range of longitude from \(-74.0108\) to \(-73.9600\) and latitude from \(40.7333\) to \(40.7700\) in New York City. Within the time and space selected for the experiment, the number of taxi trajectory data was about 9 million, the number of point-of-interest data was 1612, and there were 18 types of points of interest. In this study, we designed ablation experiments to verify the effectiveness of the HMPE algorithm. In this paper, a method similar to unsupervised learning verification was adopted to evaluate the learning effect of the model on target hidden vectors using the performance results of the model in downstream tasks. The HMPE algorithm framework proposed in this paper mainly needs to verify two parts: the human mobility pattern extraction module and the timing feature extraction module. Therefore, the control part of the ablation experiment was set as the representative algorithm of each part. For the human mobility pattern extraction module, there are two parts in the framework; one is the graph representation algorithm with attributes, and the other is ReadOut. Consider the Node2 Vec algorithm [44] (Scalable Network Feature Learning) as a representative of a class of algorithms for attribute-free, purely structured representations. We used ANRL [45] (Deep-Neural-Network-Based Attribute Network Representation Learning) to compare the differences between attribute-based graph-convolution algorithms and attribute-based random-walk algorithms. In the ReadOut section, we used a convolutional neural network to verify the difference between city-wide global view (MLP) and local view (CNN [46]) in urban spatio-temporal scenes. For the time series feature-extraction module, the GRU [47] in the long-term and short-term memory network was used as the representative algorithm to compare the difference between learning only a single long-term and short-term memory vector and using the attention mechanism to treat the features of each time period differently in the urban spatio-temporal scene [48,49]. In the experiment, we set 200 epochs, the test set ratio was set to 0.3, the training batch size was set to four, the side length of the city raster division was set to 16, and the time series length was set to 24. In addition, in order to ensure the signleens of the ablation experiment variables, we limited the size of the output vector of the human mobility feature module to 32 and set the number of channels to eight. The computational focus of the experiment lies primarily in the encoding and decoding modules of the multi-head attention mechanism. The architecture of this module is inspired by the encoding and decoding structure of the transformer, so the time complexity is similar. The time complexity of the transformer model primarily depends on the computational complexity of its self-attention mechanism and feed-forward neural network. The time complexity per layer is approximately \(O(n^{2}d)\), where \(n\) represents the sequence length and \(d\) represents the representation dimension. We use Mean Absolute Percentage Error (MAPE) and Rooted Mean Square Error (RMSE) as the evaluation indicators of the framing effect. The experimental results are shown in Table 1, and it can be seen that the performance of the HMPE framework proposed in this paper is better than other baseline models in terms of RMSE and MAPE.