## 4 Experiments and Results Analysis
### Experimental Results
#### 4.4.2 POI Group Recommendation Results  
This part will present the experimental results of this group recommendation algorithm and other three comparative experiments. Each group is composed of multiple  
Figure 4: Parameter \(\alpha\) for multi-person group. Figure 3: Parameter \(\alpha\) for two-person group. users, so the overall preference of a group for a POI is mainly determined by the different preferences of each member in the group. In this experiment, for groups with different number of members, we divided them into two-person group and multi-person group to carry out experimental verification analysis. (1) The experimental results for the two-person group. Figure 5 plots the recommendation performance of the four algorithms for the two-person group on the NYC dataset. _Precision@N_ and _Recall@N_ results of different recommendation algorithms are depicted in Figure 5a,b, respectively. With the increase in the number of recommended POI from 5 to 20, the accuracy presents a gradual downward trend, while the recall rate presents a gradual upward trend. It can be seen from the figure that the experimental results of CFL and CFA based on collaborative filtering with recommendation strategy are not good. Furthermore, the tendency of the histogram of CFL and CFA is nearly the same for \(N=5\). Those two algorithms employ different strategies of aggregating each group member's preference. However, there are differences in group recommendation performance under different conditions. The adoption of recommendation strategy and the recommendation results of group members may destroy the integrity of the group, and the preferences of group members are influenced by others in the group, so there are differences between group members' preferences when they are in a group and those who are not in the group. CMFC algorithm aggregates individual recommendation results, and it calculates group differences by adding group divergence function, so it has better performance than CFL and CFA from the experimental results. In this paper, different from CMFC algorithm generating a list of top-N recommendation POIs for each user in the group, CFID takes the group as a whole through the establishment of group preference model, and CFID combines collaborative filtering algorithm with intragroup divergence computation. Therefore, the final group recommendation of CFID takes into account the integrity of the group and the difference of group members' preferences. CFID is obviously better than the other three comparative test methods in accuracy and recall. Detailed experimental results on the New York dataset are described in Table 3. It is found that _Precision@N_ and _Recall@N_ of ours are higher than the other three algorithms when \(N\) is different. In a word, it shows that as \(N\) is changing from 5 to 20, the results of our method become slightly better. When the value of \(N\) is small, the algorithms can obtain better precision, and when the value of \(N\) is large, the algorithm can obtain better recall results. CFID gets the highest precision value of 0.06 when the number of recommendation location \(N\) is 10, and CFID obtains the highest recall value of 0.618 when the number of recommendation location \(N\) is 10. Take \(N=5\) for example, the precision of CFID is 0.1124, which is much higher than 0.0658, 0.0666 and 0.0818 of CFL, CFA and CMFC, respectively. So our algorithm nearly improves by 70.8%, 68.7% and 37.4% than CFL, CFA and CMFC. Meanwhile, the recall of our method is about 0.562 when \(N=5\), which is much higher than 0.3294 and 0.4092 of CFL and CMFC, respectively. Therefore, our algorithm  
Figure 5: Two-person group recommendation results of four algorithms on New York City dataset. (**a**) precision result; (**b**) recall result. nearly improves by 70.6% and 37.3% than CFL and CMFC. The results indicate that CFID algorithm performs better than other three algorithms on New York City dataset.