While recommender systems have significantly benefited from implicit feedback, they have often missed the nuances of multi-behavior interactions between users and items. Historically, these systems either amalgamated all behaviors, such as \textit{impression} (formerly \textit{view}), \textit{add-to-cart}, and \textit{buy}, under a singular 'interaction' label, or prioritized only the target behavior, often the \textit{buy} action, discarding valuable auxiliary signals. Although recent advancements tried addressing this simplification, they primarily gravitated towards optimizing the target behavior alone, battling with data scarcity. Additionally, they tended to bypass the nuanced hierarchy intrinsic to behaviors. To bridge these gaps, we introduce the \textbf{H}ierarchical \textbf{M}ulti-behavior \textbf{G}raph Attention \textbf{N}etwork (HMGN). This pioneering framework leverages attention mechanisms to discern information from both inter and intra-behaviors while employing a multi-task Hierarchical Bayesian Personalized Ranking (HBPR) for optimization. Recognizing the need for scalability, our approach integrates a specialized multi-behavior sub-graph sampling technique. Moreover, the adaptability of HMGN allows for the seamless inclusion of knowledge metadata and time-series data. Empirical results attest to our model's prowess, registering a notable performance boost of up to 64\% in NDCG@100 metrics over conventional graph neural network methods.

翻译：尽管推荐系统已从隐式反馈中获益良多，但往往忽视了用户与物品之间多行为交互的细微差别。以往系统要么将所有行为（如印象（原为"浏览"）、加入购物车和购买）笼统归为单一"交互"标签，要么仅优先考虑目标行为（通常是购买行为），丢弃了有价值的辅助信号。虽然近期研究试图解决这一简化问题，但主要倾向于仅优化目标行为，难以应对数据稀疏性。此外，这些研究往往忽略了行为之间固有的层级结构。为弥补这些不足，我们提出了层级多行为图注意力网络（HMGN）。这一开创性框架利用注意力机制从行为间与行为内两个维度提取信息，同时采用多任务层级贝叶斯个性化排序（HBPR）进行优化。考虑到可扩展性需求，我们的方法集成了专门的多行为子图采样技术。此外，HMGN的适应性使其能无缝融入知识元数据和时间序列数据。实验结果表明，我们的模型性能卓越，与传统图神经网络方法相比，在NDCG@100指标上实现了高达64%的显著提升。