Recently, making recommendations for ephemeral groups which contain dynamic
users and few historic interactions have received an increasing number of
attention. The main challenge of ephemeral group recommender is how to
aggregate individual preferences to represent the group's overall preference.
Score aggregation and preference aggregation are two commonly-used methods that
adopt hand-craft predefined strategies and data-driven strategies,
respectively. However, they neglect to take into account the importance of the
individual inherent factors such as personality in the group. In addition, they
fail to work well due to a small number of interactive records. To address
these issues, we propose a Personality-Guided Preference Aggregator (PEGA) for
ephemeral group recommendation. Concretely, we first adopt hyper-rectangle to
define the concept of Group Personality. We then use the personality attention
mechanism to aggregate group preferences. The role of personality in our
approach is twofold: (1) To estimate individual users' importance in a group
and provide explainability; (2) to alleviate the data sparsity issue that
occurred in ephemeral groups. The experimental results demonstrate that our
model significantly outperforms the state-of-the-art methods w.r.t. the score
of both Recall and NDCG on Amazon and Yelp datasets