## 5 Conclusions  
In this paper, a collaborative filtering algorithm with intragroup divergence for POI group recommendation is proposed. The innovation of this paper is mainly in three directions. Firstly, user preference vector is constructed based on the preference of user on time and category. In the aspect of group integrity, CFID algorithm takes the group as a virtual user, and a group feature preference model is established based on user's category and temporal preference model. Secondly, we compute the similarity of the group and other users based on the preference vector and the users' check-in information. Thirdly, in the aspect of calculating intragroup divergence, we develop a novel group divergence computation method for candidate POIs by analyzing the preference probability of all members in the group and their friends. It improves the result significantly by comprehensively considering the group integrity and difference. It studies the influence of group size on the results of group recommendation. The recommendation results of two-person group and multi-person group on two real datasets of Foursquare show that  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{\(Precision@N\)} & \multicolumn{3}{c}{\(Recall@N\)} \\ \cline{2-7}  & **5** & **10** & **20** & **5** & **10** & **20** \\ \hline CFL & 0.0800 & 0.0515 & 0.0321 & 0.4001 & 0.5154 & 0.6423 \\ CFA & 0.0831 & 0.0581 & 0.0358 & 0.4154 & 0.5808 & 0.7154 \\ CMFC & 0.0915 & 0.0588 & 0.0356 & 0.4577 & 0.5885 & 0.7115 \\ CFID & 0.1015 & 0.0646 & 0.0396 & 0.5077 & 0.6462 & 0.7923 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative results for multi-person group on Tokyo dataset.  
Figure 8: Multi-person group recommendation results of four algorithms on Tokyo dataset. (**a**) precision result; (**b**) recall result.  
Figure 7: Multi-person group recommendation results of four algorithms on New York City dataset. (**a**) precision result; (**b**) recall result.  
CFID algorithm has a significant improvement in terms of precision and recall compared with the other methods.  
The next direction of the work is try to investigate other factors to construct the user preference model, and we will study POI recommendation algorithm on the basis of machine learning and deep learning technology.  
Conceptualization, M.Y. and X.Z.; methodology, M.Y.; validation, M.Y. and X.Z.; formal analysis, M.Y.; writing--original draft preparation, M.Y. and X.Z.; writing--review and editing, X.Z.; supervision, Y.L.; funding acquisition, Y.L. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under grants 61806083, 61872158; Excellent Young Talents Program for the Department of Science and Technology of Jilin Province of China under grant 20190103051 JH.  
Not Applicable.  
**Data Availability Statement**: Publicly available datasets were analyzed in this study. These data can be found here: [[https://www.kaggle.com/chetanism/foursquare-nyc-and-tokyo-checkin-dataset/version/1](https://www.kaggle.com/chetanism/foursquare-nyc-and-tokyo-checkin-dataset/version/1)]([https://www.kaggle.com/chetanism/foursquare-nyc-and-tokyo-checkin-dataset/version/1](https://www.kaggle.com/chetanism/foursquare-nyc-and-tokyo-checkin-dataset/version/1)).  
The authors declare no conflict of interest.