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.

翻译：近期，针对包含动态用户且历史交互较少的短暂群体进行推荐的研究日益受到关注。短暂群体推荐的主要挑战在于如何聚合个体偏好以表征群体的整体偏好。评分聚合与偏好聚合是两种常用方法，分别采用手工预定义策略和数据驱动策略。然而，这两种方法均忽略了群体中个体内在因素（如个性）的重要性。此外，由于交互记录数量稀少，其性能表现欠佳。为解决上述问题，我们提出了一种面向短暂群体推荐的个性引导偏好聚合器（PEGA）。具体而言，我们首先采用超矩形定义群体个性的概念，进而利用个性注意力机制聚合群体偏好。个性在我们的方法中发挥双重作用：（1）评估群体中个体用户的重要性并提供可解释性；（2）缓解短暂群体中存在的数据稀疏问题。实验结果表明，在Amazon和Yelp数据集上，我们的模型在Recall和NDCG指标上均显著优于现有最先进的方法。