  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.
