  Online groups have become increasingly prevalent, providing users with space
to share experiences and explore interests. Therefore, user-centric group
discovery task, i.e., recommending groups to users can help both users' online
experiences and platforms' long-term developments. Existing recommender methods
can not deal with this task as modeling user-group participation into a
bipartite graph overlooks their item-side interests. Although there exist a few
works attempting to address this task, they still fall short in fully
preserving the social context and ensuring effective interest representation
learning.
  In this paper, we focus on exploring the intents that motivate users to
participate in groups, which can be categorized into different types, like the
social-intent and the personal interest-intent. The former refers to users
joining a group affected by their social links, while the latter relates to
users joining groups with like-minded people for self-enjoyment. To comprehend
different intents, we propose a novel model, DiRec, that first models each
intent separately and then fuses them together for predictions. Specifically,
for social-intent, we introduce the hypergraph structure to model the
relationship between groups and members, leading to a richer understanding of
the social context. As for interest-intent, we employ novel structural
refinement on the interactive graph to uncover more intricate user behaviors
and group interests, realizing better representation learning of interests.
Furthermore, we also observe the intent overlapping in real-world scenarios and
devise a novel self-supervised learning loss that encourages such alignment for
final recommendations. Extensive experiments on three public datasets show the
significant improvement of DiRec over the state-of-the-art methods.
