  Social activities play an important role in people's daily life since they
interact. For recommendations based on social activities, it is vital to have
not only the activity information but also individuals' social relations.
Thanks to the geo-social networks and widespread use of location-aware mobile
devices, massive geo-social data is now readily available for exploitation by
the recommendation system. In this paper, a novel group recommendation method,
called attentive geo-social group recommendation, is proposed to recommend the
target user with both activity locations and a group of users that may join the
activities. We present an attention mechanism to model the influence of the
target user $u_T$ in candidate user groups that satisfy the social constraints.
It helps to retrieve the optimal user group and activity topic candidates, as
well as explains the group decision-making process. Once the user group and
topics are retrieved, a novel efficient spatial query algorithm SPA-DF is
employed to determine the activity location under the constraints of the given
user group and activity topic candidates. The proposed method is evaluated in
real-world datasets and the experimental results show that the proposed model
significantly outperforms baseline methods.
