  Group recommendation provides personalized recommendations to a group of
users based on their shared interests, preferences, and characteristics.
Current studies have explored different methods for integrating individual
preferences and making collective decisions that benefit the group as a whole.
However, most of them heavily rely on users with rich behavior and ignore
latent preferences of users with relatively sparse behavior, leading to
insufficient learning of individual interests. To address this challenge, we
present the Multi-Granularity Attention Model (MGAM), a novel approach that
utilizes multiple levels of granularity (i.e., subsets, groups, and supersets)
to uncover group members' latent preferences and mitigate recommendation noise.
Specially, we propose a Subset Preference Extraction module that enhances the
representation of users' latent subset-level preferences by incorporating their
previous interactions with items and utilizing a hierarchical mechanism.
Additionally, our method introduces a Group Preference Extraction module and a
Superset Preference Extraction module, which explore users' latent preferences
on two levels: the group-level, which maintains users' original preferences,
and the superset-level, which includes group-group exterior information. By
incorporating the subset-level embedding, group-level embedding, and
superset-level embedding, our proposed method effectively reduces group
recommendation noise across multiple granularities and comprehensively learns
individual interests. Extensive offline and online experiments have
demonstrated the superiority of our method in terms of performance.
