<p>Recommending suitable items to a group of users, commonly referred to as group recommendation, is becoming increasingly urgent with the development of group activities (e.g., building a learning group for a goal). However, the group recommendation still faces two challenges: (1) sparsity problems due to the lack of user/group-item interactions; and (2) aggregating the individual preferences of group members into the group’s preferences. To overcome these challenges, we propose a novel group recommendation model called Dependency Relationships-Enhanced Attentive Group Recommendation (<span>DREAGR</span>). Specifically, we introduce dependency relationships between items as side information to enhance the user/group-item interactions and alleviate the interaction sparsity problem. Then, we propose a Path-Aware Attention Embedding method to obtain users’ preferences for different types of paths, and 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. We conducted experiments on two real-world datasets to demonstrate the superiority of <span>DREAGR</span> by comparing it with state-of-the-art group recommender models. The experimental results show that <span>DREAGR</span> outperforms other models, especially HR@N and NDCG@N (N=5, 10, 20), where the HR@5 and NDCG@ 5 of <span>DREAGR</span> have improved by 4.83%, 3.46%, 7.59%, and 4.51% on both datasets, respectively.</p>