  The task of recommending items to a group of users, a.k.a. group
recommendation, is receiving increasing attention. However, the cold-start
problem inherent in recommender systems is amplified in group recommendation
because interaction data between groups and items are extremely scarce in
practice. Most existing work exploits associations between groups and items to
mitigate the data scarcity problem. However, existing approaches inevitably
fail in extreme cold-start scenarios where associations between groups and
items are lacking. For this reason, we design a group recommendation model for
EXreme cold-star} in group REcommendation (EXTRE) suitable for the extreme cold
start scenario. The basic idea behind EXTRE is to use the limit theory of graph
convolutional neural networks to establish implicit associations between groups
and items, and the derivation of these associations does not require explicit
interaction data, making it suitable for cold start scenarios. The training
process of EXTRE depends on the newly defined and interpretable concepts of
consistency and discrepancy, other than commonly used negative sampling with
pairwise ranking, which can improve the performance of the group
recommendation. Extensive experiments validate the efficacy of the proposed
model EXTRE.
