## 4 Experiments and Results Analysis
### Experimental Results
#### 4.4.2 POI Group Recommendation Results
Figure 6 shows the experimental comparison of two-person group recommendation results of four algorithms on Tokyo dataset. Compared with the baseline methods, our proposed POI group recommendation has better recommendation performance. Though the dataset TK is much larger than the dataset NYC, in the case of data overload, the other three experimental methods have a significant decline. For example, when the number of recommended locations is 10, the recall rate of CFA is 0.4477 for NYC dataset, while it is only 0.28 for TK dataset, and the performance of CMFC algorithm reduced from 0.5075 to 0.28. The recall of CFID is about 0.52 for TK dataset. CFID still performs well when the dataset is large. In a word, the experimental results of CFID have a greater improvement than the other three experimental algorithms. Detailed experimental results on Tokyo dataset are described in Table 4. Note that precision and recall value obtained by our proposed method on Tokyo dataset are higher than other three algorithms. It is seen that precision value decreases and recall value increases, respectively, with the number of location recommendations varying from 5 to 20. Especially, take \(N=10\) for example, the precision of our method is about 0.0522, which is larger than 0.0247, 0.0283 and 0.0287 of CFL,CFA and CMFC, respectively. Therefore, the CFID algorithm nearly increases by 111.3%, 84.5% and 81.9%, respectively. Meanwhile, the recall of our method is about 0.6186 which is much higher than 27% and 20% of CFL and CMFC algorithm when \(N=20\), respectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{3}{c}{\(Precision@N\)} & \multicolumn{3}{c}{\(Recall@N\)} \\ \cline{2-7}  & **5** & **10** & **20** & **5** & **10** & **20** \\ \hline CFL & 0.0658 & 0.0430 & 0.02745 & 0.3294 & 0.4297 & 0.5489 \\ CFA & 0.0666 & 0.0448 & 0.0288 & 0.3328 & 0.4477 & 0.5752 \\ CMFC & 0.0818 & 0.0508 & 0.0313 & 0.4092 & 0.5075 & 0.6268 \\ CFID & 0.1124 & 0.0618 & 0.0330 & 0.5620 & 0.6180 & 0.6599 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparative results for two-person group on New York City dataset  
Figure 6: Two-persongroup recommendation results of four algorithms on Tokyo dataset. (**a**) precision result; (**b**) recall result. (2) The results of the multi-person group experiment. Table 5 shows the detailed results of the algorithm comparison on NYC dataset. Take \(N=5\) for example, the precision of our method is about 0.1112, and the corresponding value of CFL, CFA and CMFC are 0.0567, 0.0727 and 0.0843, respectively. Therefore, is CFID algorithm results are nearly 5.4%, 3.8% and 2.7% higher than CFL, CFA and CMFC, respectively. The improvement results indicate that the CFID algorithm performance is the best. Figure 7 is a histogram of the _Precision@N_ and _Recall@N_ of the four comparison algorithms on the NYC dataset. Comparing CFL with CFA, It is seen that when the group size is two, the difference between the average satisfaction strategy and the least misery satisfaction strategy is slight. When the number of members in a group is more than two, the average strategy is much higher than the least misery strategy, which shows that when there are more members in a group, CFA algorithms employing the average strategy considers all members in the group, and it performs better than CFL algorithm which considers only one member in the group. Furthermore, comparing the recommendation results of two-person group and multi-person group on NYC, the CFID method has higher accuracy and recall rate than the other three methods. The detailed comparative results of four algorithms on Tokyo dataset are listed in Table 6. CFID algorithm has the highest accuracy and recall compared with CFL, CFA and CMFC. Significantly, the precision of ours is 0.0646 when \(N=10\). Meanwhile, the precision value of ours when \(N=10\) nearly increases by 25.4%, 11.2% and 9.86% than CFL, CFA and CMFC algorithm, respectively. The improvement results indicate our algorithm performs better than others.