###### Abstract  
The purpose of POI group recommendation is to generate a recommendation list of locations for a group of users. Most of the current studies first conduct personal recommendation and then use recommendation strategies to integrate individual recommendation results. Few studies consider the divergence of groups. To improve the precision of recommendations, we propose a POI group recommendation method based on collaborative filtering with intragroup divergence in this paper. Firstly, user preference vector is constructed based on the preference of the user on time and category. Furthermore, a computation method similar to TF-IDF is presented to compute the degree  
use statistical techniques to get neighbor users. It makes use of the behavior of neighbor users to predict the preferences of target user, and recommends top-N locations to target user. However, in real scenarios, group activities are becoming more popular in various fields today. Users will always like to go to a place together, such as go to a basketball gym together for fun, or go to the library together for study. It is more necessary to recommend a number of POIs to a group of users than to an individual user. Group recommendation systems are used to generate ranked lists of recommendations based on users' preferences [3]. Many group recommendation systems are applied in different fields, such as tourism, music, movies and so on. For example, Where2 Eat is a model for group restaurant recommendation [4], and HappyMovie is a group recommendation model applying for the domain of movie. The main challenge in the study of group recommendation systems is to reconcile the different preference of a group's members. Group recommendation methods have usually used the aggregation functions to model the preferences of group members. Some group recommendation results can be obtained by aggregating results of individual recommendation. [PERSON] summarized three strategies for aggregating individual recommendations: average satisfaction [5], least misery [6] and maximum satisfaction. The average strategy takes the average recommendation score of all group members as the final score. However, different users have different preferences for items, and average strategy may lead to inaccurate results. In order to satisfy everyone, least misery adopts the minimum recommendation scores as the final score. [PERSON] et al. [7] compares different aggregation strategies, and finds the effectiveness of the strategy depends on the group size. In addition, some group recommendation is based on aggregating the user' individual preference into a group preference [8]. This kind of approach treats the group as a user, and every user in the group has an influence for the result. However, the computation of weight of the user in the group is a challenging problem. Where-to-go recommendation systems can realize group recommendations based on random walk with a restart approach, but the influence weight of the members of the group is equal in this system [9]. However, group members should have different influences on the final decision of the group and the system needs to calculate the weight of different members for aggregating members' preferences. Effectively aggregating the individual preferences of group members to recommend the most meaningful items for the whole group is necessary. Meanwhile, to improve the quality of group recommendations, more additional influential features are taken into account, such as geographical factors, temporal feature, category factors, social factors and experience content. Those influential features should be integrated and mined to improve the effectiveness of POI recommendation. Different people tend to visit different places at different time, so we can mine the user's preference of locations with time factors. The social factors between members are considered to improve the performance of group recommendation [10]. [PERSON] proposed the first group event venue recommendation (GEVR) system [11].