  We study the problem of making item recommendations to ephemeral groups,
which comprise users with limited or no historical activities together.
Existing studies target persistent groups with substantial activity history,
while ephemeral groups lack historical interactions. To overcome group
interaction sparsity, we propose data-driven regularization strategies to
exploit both the preference covariance amongst users who are in the same group,
as well as the contextual relevance of users' individual preferences to each
group.
  We make two contributions. First, we present a recommender
architecture-agnostic framework GroupIM that can integrate arbitrary neural
preference encoders and aggregators for ephemeral group recommendation. Second,
we regularize the user-group latent space to overcome group interaction
sparsity by: maximizing mutual information between representations of groups
and group members; and dynamically prioritizing the preferences of highly
informative members through contextual preference weighting. Our experimental
results on several real-world datasets indicate significant performance
improvements (31-62% relative NDCG@20) over state-of-the-art group
recommendation techniques.
