Recommending suitable items to a group of users, commonly referred to as the group recommendation task, is becoming increasingly urgent with the development of group activities. The challenges within the group recommendation task involve aggregating the individual preferences of group members as the group's preferences and facing serious sparsity problems due to the lack of user/group-item interactions. To solve these problems, we propose a novel approach called Dependency Relationships-Enhanced Attentive Group Recommendation (DREAGR) for the recommendation task of occasional groups. Specifically, we introduce the dependency relationship between items as side information to enhance the user/group-item interaction and alleviate the interaction sparsity problem. Then, we propose a Path-Aware Attention Embedding (PAAE) method to model users' preferences on different types of paths. Next, we design a gated fusion mechanism to fuse users' preferences into their comprehensive preferences. Finally, we develop an attention aggregator that aggregates users' preferences as the group's preferences for the group recommendation task. We conducted experiments on two datasets to demonstrate the superiority of DREAGR by comparing it with state-of-the-art group recommender models. The experimental results show that DREAGR outperforms other models, especially HR@N and NDCG@N (N=5, 10), where DREAGR has improved in the range of 3.64% to 7.01% and 2.57% to 3.39% on both datasets, respectively.

翻译：群体推荐任务旨在为群体推荐合适的项目，随着群体活动的日益频繁，该任务的需求愈发迫切。群体推荐中的挑战涉及如何聚合群体成员的个人偏好以形成群体偏好，同时面临因用户/群体-项目交互缺失导致的严重稀疏性问题。为解决这些问题，我们提出一种面向临时群体推荐任务的新方法——基于依赖关系增强的群体注意力推荐（DREAGR）。具体而言，我们引入项目间的依赖关系作为辅助信息，以增强用户/群体-项目交互，缓解交互稀疏性问题。随后，提出一种路径感知注意力嵌入方法（PAAE）来建模用户在不同路径类型上的偏好。接着，设计门控融合机制将用户偏好融合为综合偏好。最后，构建注意力聚合器将用户偏好聚合为群体偏好以完成群体推荐任务。在两个数据集上的实验表明，与最先进的群体推荐模型相比，DREAGR展现出优越性。实验结果显示，DREAGR在所有模型中表现最佳，特别是在HR@N和NDCG@N（N=5,10）指标上，在两个数据集上分别提升了3.64%-7.01%和2.57%-3.39%。