  Frequent group activities of human beings have become an indispensable part
in their daily life. Group recommendation can recommend satisfactory activities
to group members in the recommender systems, and the key issue is how to
aggregate preferences in different group members. Most existing group
recommendation employed the predefined static aggregation strategies to
aggregate the preferences of different group members, but these static
strategies cannot simulate the dynamic group decision-making. Meanwhile, most
of these methods depend on intuitions or assumptions to analyze the influence
of group members and lack of convincing theoretical support. We argue that the
influence of group members plays a particularly important role in group
decision-making and it can better assist group profile modeling and perform
more accurate group recommendation. To tackle the issue of preference
aggregation for group recommendation, we propose a novel attentive aggregation
representation learning method based on sociological theory for group
recommendation, namely SIAGR (short for "Social Influence-based Attentive Group
Recommendation"), which takes attention mechanisms and the popular method
(BERT) as the aggregation representation for group profile modeling.
Specifically, we analyze the influence of group members based on social
identity theory and two-step flow theory and exploit an attentive mavens mining
method. In addition, we develop a BERT-based representation method to learn the
interaction of group members. Lastly, we complete the group recommendation
under the neural collaborative filtering framework and verify the effectiveness
of the proposed method by experimenting.
