## 4 Experiments and Results Analysis
### Experimental Results  
#### 4.4.1 Parameter Setting  
Choosing the optimal parameter can maintain a high precision value for recommendation. The experimental results of optimizing the threshold parameter \(\theta\) for New York City dataset and Tokyo dataset are shown in Figures 1 and 2, respectively. In order to explore the influence of optimization parameter threshold \(\theta\) on group recommendation, we tune the threshold from 0.4 to 0.9 by step 0.1 for a two-person group to calculate the influence of threshold \(\theta\) on recommendation accuracy. It takes precision as the objective function to determine the parameter. As shown in Figures 1 and 2, with the threshold parameter \(\theta\) changing, the recommendation results show a trend of first increasing and then decreasing. In fact, the threshold value is for selecting similar users. The higher the value is, the similar the users are. The users with high similarity to the group (virtual user) can give more suggestions, and their opinions can be adopted by the group. When the threshold is low, the POI preferences of some users who are no longer particularly similar to the group are also recommended, thus reducing the accuracy of recommendation results. While the threshold is too large, some preference information of POIs is lost, and the selected similar users cannot recommend enough POIs for the group very well, so the performance declines. From Figures 1 and 2, the optimal parameter \(\theta\) is the experiment is 0.7 and 0.5 for New York City and Tokyo dataset separately.  
and they do not have a common hobby, the disagreement between any two members in the group is considerable and complicated. In view of this situation, the number of users in the group affects the recommendation results. We divide the group recommendation into two-person group and multi-person group, and recommend their locations in the experiment section.  
The parameter of the algorithm is tuned for achieving optimal performance. Here, we use a simple heuristic method to choose the value of parameter \(\alpha\), and we adopt the strategy of maximizing the value of _Precision_@10 to determine the optimal \(\alpha\) value. Different precision values are obtained when \(\alpha\) increases from 0.5 to 1 by step 0.1 and 0.05. Different value of \(\alpha\) affects the performance of group recommendation with different group size. As is shown in Figure 3, when the parameter value \(\alpha=1\), the effect for two-person group recommendation is the best. When the number of members in the group is two, the group size is small, the relationship between users in the group is similar, and the similarity between any two members in the group is very high. This shows if a group of two users traveling together, the relationship between them is close and their hobbies are similar. They have no intragroup divergence. Furthermore, seen from Figure 4, the precision of top-10 recommendation achieves best when \(\alpha\) is set to 0.85 for NYC dataset. It is observed that the optimal \(\alpha\) is 0.9 for multi-person group recommendation for TK dataset. When there are more users in the group, the intragroup divergence will impact the recommendation result.