Sequential Recommendation (SR) captures users' dynamic preferences by modeling how users transit among items. However, SR models that utilize only single type of behavior interaction data encounter performance degradation when the sequences are short. To tackle this problem, we focus on Multi-Behavior Sequential Recommendation (MBSR) in this paper, which aims to leverage time-evolving heterogeneous behavioral dependencies for better exploring users' potential intents on the target behavior. Solving MBSR is challenging. On the one hand, users exhibit diverse multi-behavior patterns due to personal characteristics. On the other hand, there exists comprehensive co-influence between behavior correlations and item collaborations, the intensity of which is deeply affected by temporal factors. To tackle these challenges, we propose a Personalized Behavior-Aware Transformer framework (PBAT) for MBSR problem, which models personalized patterns and multifaceted sequential collaborations in a novel way to boost recommendation performance. First, PBAT develops a personalized behavior pattern generator in the representation layer, which extracts dynamic and discriminative behavior patterns for sequential learning. Second, PBAT reforms the self-attention layer with a behavior-aware collaboration extractor, which introduces a fused behavior-aware attention mechanism for incorporating both behavioral and temporal impacts into collaborative transitions. We conduct experiments on three benchmark datasets and the results demonstrate the effectiveness and interpretability of our framework. Our implementation code is released at https://github.com/TiliaceaeSU/PBAT.

翻译：序列推荐（Sequential Recommendation, SR）通过建模用户在物品间的转移模式来捕捉其动态偏好。然而，仅利用单一行为交互数据的SR模型在序列较短时会出现性能退化。针对这一问题，本文聚焦于多行为序列推荐（Multi-Behavior Sequential Recommendation, MBSR），旨在利用随时间演化的异质行为依赖关系，更深入地探索用户在目标行为上的潜在意图。解决MBSR面临挑战：一方面，由于个人特征，用户展现出多样化的多行为模式；另一方面，行为关联与物品协同之间存在复杂的相互影响关系，且其强度受时间因素的深刻影响。为应对这些挑战，我们提出一种个性化行为感知Transformer框架（Personalized Behavior-Aware Transformer, PBAT）用于MBSR问题，该框架以新颖方式建模个性化模式与多方面序列协同，从而提升推荐性能。首先，PBAT在表示层开发个性化行为模式生成器，提取动态且具判别性的行为模式用于序列学习。其次，PBAT通过行为感知协同提取器重构自注意力层，引入融合行为感知注意力机制，将行为与时间影响共同融入协同演化过程。我们在三个基准数据集上开展实验，结果证明了所提框架的有效性与可解释性。我们的实现代码已发布于https://github.com/TiliaceaeSU/PBAT。