Heterogeneous sequential recommendation (HSR) aims to learn dynamic behavior dependencies from the diverse behaviors of user-item interactions to facilitate precise sequential recommendation. Despite many efforts yielding promising achievements, there are still challenges in modeling heterogeneous behavior data. One significant issue is the inherent sparsity of a real-world data, which can weaken the recommendation performance. Although auxiliary behaviors (e.g., clicks) partially address this problem, they inevitably introduce some noise, and the sparsity of the target behavior (e.g., purchases) remains unresolved. Additionally, contrastive learning-based augmentation in existing methods often focuses on a single behavior type, overlooking fine-grained user preferences and losing valuable information. To address these challenges, we have meticulously designed a behavior-aware dual-channel preference learning framework (BDPL). This framework begins with the construction of customized behavior-aware subgraphs to capture personalized behavior transition relationships, followed by a novel cascade-structured graph neural network to aggregate node context information. We then model and enhance user representations through a preference-level contrastive learning paradigm, considering both long-term and short-term preferences. Finally, we fuse the overall preference information using an adaptive gating mechanism to predict the next item the user will interact with under the target behavior. Extensive experiments on three real-world datasets demonstrate the superiority of our BDPL over the state-of-the-art models.

翻译：异构序列推荐（HSR）旨在从用户-物品交互的多样化行为中学习动态行为依赖关系，以支持精准的序列推荐。尽管已有诸多努力取得显著成果，但在建模异构行为数据时仍面临挑战。一个关键问题在于真实世界数据的固有稀疏性，这会削弱推荐性能。虽然辅助行为（如点击）部分缓解了该问题，但不可避免地引入噪声，且目标行为（如购买）的稀疏性仍未解决。此外，现有方法中基于对比学习的增强往往聚焦于单一行为类型，忽视了细粒度用户偏好并丢失了有价值信息。为应对这些挑战，我们精心设计了一种行为感知双通道偏好学习框架（BDPL）。该框架首先构建定制化的行为感知子图以捕捉个性化行为转换关系，随后采用新型级联结构图神经网络聚合节点上下文信息。接着，我们通过偏好级对比学习范式建模并增强用户表示，同时考虑长期与短期偏好。最后，利用自适应门控机制融合整体偏好信息，预测用户在目标行为下接下来会交互的物品。在三个真实世界数据集上的大量实验表明，我们的BDPL优于现有最先进模型。