# POI Recommendation Scheme Based on User Activity Patterns and Category Similarity
## 4 Performance Evaluation
### Performance Evaluation of the Proposed Scheme
#### 4.2.2 Performance Evaluation for Inactive User Group  
The performance evaluation for the low-activity group was conducted by calculating the recommendation score using the information that could be gathered from the users. Attributes necessary for calculating the recommendation score were the user ID and latitude and longitude information, which showcases the location. When recommendations were made to the low-activity group, POIs were filtered by utilizing the universal access distance, and the recommendation scores for the remaining POIs were calculated to determine the final POI. The top k numbers of POIs were selected using the user-visited place matrix, and the user ID and filtered venue ID (referred to as POI ID) were the attributes necessary to determine the k numbers of POIs. Figure 8 shows the outcomes of the performance evaluation for a group with a low level of activity. The results reveal that the performance is higher when the number of recommendations is lower, even in the case of a low-activity group.  
Figure 8: Performance evaluation for low-activity groups.  
Figure 7: Performance evaluation for high-activity groups.  
filtering scheme, is employed for the high-activity group in the proposed scheme. This model decomposes the user-visited location matrix into low-dimensional latent factors through matrix decomposition and subsequently provides recommendations based on this information. Therefore, it can make more accurate predictions. Furthermore, the proposed scheme can achieve a high average performance owing to its ability to automatically acquire the characteristics of the data.  
Figure 10 shows the outcomes of the comparative performance evaluation for a group with a low level of activity. Compared with the Existing scheme, the proposed scheme increases the precision, recall, and F1-score by 13%, 12.7%, and 16%, respectively. The 95% confidence interval for this improvement ranged from 14% to 18%, suggesting that the proposed scheme consistently outperforms the existing scheme. The existing scheme utilizes similarity measurements to determine the degree of similarity between users and subsequently generates recommendations. By contrast, the proposed scheme generates recommendations by distinguishing comparable groups and evaluating the similarity of the user's activity pattern and the category similarity of the visited location. The outcomes of the performance evaluation confirm that the traditional collaborative filtering is less effective when an insufficient amount data are available and that the similarity of the activity pattern and the category similarity of the visited place are more helpful.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Sampling Type** & **Sampling Description** \\ \hline Random sampling & This scheme involves arbitrarily selecting samples from a dataset and utilizing these samples for learning. This scheme is useful when the dataset is small; however, an appropriate sampling scheme must be employed for large datasets. \\ \hline Stratified sampling & This scheme involves extracting samples from each stratum by dividing the dataset into strata. This approach is useful when a particular stratum plays an essential role within the dataset. \\ \hline Cluster sampling & This scheme involves partitioning the dataset into multiple clusters and subsequently selecting a random subset of the clusters for sampling. This scheme can save the time and resources necessary for sampling when the dataset is large. \\ \hline Systematic sampling & This scheme involves extracting samples from the dataset at regular intervals. This scheme can be helpful when the dataset is not large. \\ \hline \hline \end{tabular}
\end{table}
Table 12: Sampling scheme.  
Figure 9: Performance evaluation for high-activity groups.