Sequential recommendation (SR) aims to predict the next purchasing item according to users' dynamic preference learned from their historical user-item interactions. To improve the performance of recommendation, learning dynamic heterogeneous cross-type behavior dependencies is indispensable for recommender system. However, there still exists some challenges in Multi-Behavior Sequential Recommendation (MBSR). On the one hand, existing methods only model heterogeneous multi-behavior dependencies at behavior-level or item-level, and modelling interaction-level dependencies is still a challenge. On the other hand, the dynamic multi-grained behavior-aware preference is hard to capture in interaction sequences, which reflects interaction-aware sequential pattern. To tackle these challenges, we propose a Multi-Grained Preference enhanced Transformer framework (M-GPT). First, M-GPT constructs a interaction-level graph of historical cross-typed interactions in a sequence. Then graph convolution is performed to derive interaction-level multi-behavior dependency representation repeatedly, in which the complex correlation between historical cross-typed interactions at specific orders can be well learned. Secondly, a novel multi-scale transformer architecture equipped with multi-grained user preference extraction is proposed to encode the interaction-aware sequential pattern enhanced by capturing temporal behavior-aware multi-grained preference . Experiments on the real-world datasets indicate that our method M-GPT consistently outperforms various state-of-the-art recommendation methods.

翻译：序列推荐（SR）旨在根据从用户历史交互中学习到的动态偏好，预测其下一个可能购买的项目。为提升推荐性能，学习动态的异构跨类型行为依赖对推荐系统至关重要。然而，多行为序列推荐（MBSR）仍面临若干挑战。一方面，现有方法仅在行为级别或项目级别建模异构多行为依赖，而交互级别的依赖建模仍具挑战性。另一方面，交互序列中难以捕捉动态的多粒度行为感知偏好，这反映了交互感知的序列模式。为应对这些挑战，我们提出了一个多粒度偏好增强Transformer框架（M-GPT）。首先，M-GPT在序列中构建历史跨类型交互的交互级别图。随后通过重复执行图卷积来推导交互级别的多行为依赖表示，从而有效学习特定顺序下历史跨类型交互间的复杂关联。其次，我们提出了一种配备多粒度用户偏好提取机制的新型多尺度Transformer架构，通过捕捉时序行为感知的多粒度偏好来增强编码交互感知的序列模式。在真实数据集上的实验表明，我们的方法M-GPT持续优于多种最先进的推荐方法。