## 3 Methodology
### Geo-Categorical-Based POI-Group-Embedding Module  
This section presents a strategy for categorizing points of interest (POIs) based on their geographic and categorical information. Initially, the POIs are divided into regions based on their latitude and longitude characteristics. Then, they are grouped into different geographic regions. As noted in [29], in reality, users are more likely to revisit a region than  
Figure 3: Example of forming time trajectory vector. a specific POI. When users do visit POIs, it is not necessarily because they are attracted to the POIs themselves, but rather because they are interested in the region. To provide more insight into the importance of grouping POIs based on the region and category hierarchy, we are aided by two charts. Figure 4a shows in detail the distribution of the numbers of POIs across the 30 regions. It is evident from the chart that there are significant differences in the numbers of POIs in different regions. For example, the region with ID 20 tends to have many POI check-ins due to intensive user activity, while the region with ID 24 has relatively few. This distribution pattern reflects the degree of prosperity and user activity in different regions, which is essential for POI grouping. Figure 4b reveals the distribution of the numbers of POIs in different category classes within a given area. The category rank is calculated based on the ratio of the frequency of user check-ins in that category to the total number of check-ins. We can see from the chart that even within the same region, POIs in different categories may have different category ranks. Accordingly, this study developed a geo-categorical-based POI-group-embedding module, which uses the embedding vectors of the regions and categories to which the target POI belongs to represent the spatial features. Figure 5 below illustrates the grouping process of the POIs. The POIs are divided into different geographic regions and graded according to the characteristics of their categories in each region. This helps to understand the interest in and importance of the different categories of POIs in each region in a more effective way. The grading is based on the obvious quantitative differences in the categories of POIs in each region. In short, with this grouping approach, we can better capture the attributes of these POIs and the access patterns of users to them. Even POIs that are accessed less frequently, as long as they represent a certain domain region and category rank value, will still be included in the corresponding POI group and have a chance to be recommended to the target users. Our prediction task is to predict the probability of a target user visiting a specific group from the POI-group level to generate the final POI recommendation list. This method not only improves the prediction efficiency but also improves the recommendation accuracy by aggregating similar POIs. At the same time, infrequently visited POIs may have higher representation in some specific POI groups, and thus they also have a chance to be prioritized higher in the recommendation list. We model spatial features using POI groups that consist of region and category features. Figure 4: (**a**) Distribution of the numbers of POIs in different regions and (**b**) different category levels in the same region. The region division of the POIs based on latitude and longitude information is calculated by the following Equations (2)-(4):  
\[Lat^{p}=\left\lfloor\frac{Lat_{0}^{p}\times\alpha}{\alpha}\right\rfloor \tag{2}\]  
\[Lon^{p}=\left\lfloor\frac{Lon_{0}^{p}\times\alpha}{\alpha}\right\rfloor \tag{3}\]  
\[r^{p}=Cat(Lat^{p},Lon^{p}) \tag{4}\]  
where \(\alpha\) is a pre-determined parameter employed to regulate the precision of the decimal places; \(Lat_{0}^{p}\) and \(Lon_{0}^{p}\) represent the original latitude and longitude of the POI (p), respectively; \(Lat_{0}^{p}\) and \(Lon^{p}\) represent the rounded latitude and longitude of the POI (p), respectively; \(r^{p}\) represents a specific region.