STAR-Rec：调和序列推荐中的长度差异与模式多样性
STAR-Rec: Making Peace with Length Variance and Pattern Diversity in Sequential Recommendation
AI总结：
STAR-Rec提出序列级混合专家框架，结合偏好感知注意力与状态空间建模，以线性复杂度处理变长序列并自适应路由多样化行为模式，在四个真实数据集上一致超越现有序列推荐方法。
摘要：
近年来，深度序列推荐模型往往难以有效建模用户行为的关键特征，尤其是在处理序列长度变化和捕捉多样化交互模式方面。我们提出STAR-Rec，一种新颖的架构，通过序列级混合专家框架协同结合偏好感知注意力与状态空间建模。STAR-Rec通过以下方式应对这些挑战：（1）采用偏好感知注意力来捕捉内在相似的物品关系与多样化偏好；（2）利用状态空间建模以线性复杂度高效处理变长序列；（3）引入混合专家组件，将不同行为模式自适应路由至专门的专家，同时处理聚焦的类别特定浏览和多样化类别探索模式。我们从理论上证明了状态空间模型与注意力机制在推荐场景中如何被自然统一，其中SSM通过状态压缩捕捉时间动态，而注意力同时建模相似和多样化的物品关系。在四个真实世界数据集上的大量实验表明，STAR-Rec一致地优于最先进的序列推荐方法，特别是在涉及多样化用户行为和变化序列长度的场景中。
英文摘要：
Recent deep sequential recommendation models often struggle to effectively model key characteristics of user behaviors, particularly in handling sequence length variations and capturing diverse interaction patterns. We propose STAR-Rec, a novel architecture that synergistically combines preference-aware attention and state-space modeling through a sequence-level mixture-of-experts framework. STAR-Rec addresses these challenges by: (1) employing preference-aware attention to capture both inherently similar item relationships and diverse preferences, (2) utilizing state-space modeling to efficiently process variable-length sequences with linear complexity, and (3) incorporating a mixture-of-experts component that adaptively routes different behavioral patterns to specialized experts, handling both focused category-specific browsing and diverse category exploration patterns. We theoretically demonstrate how the state space model and attention mechanisms can be naturally unified in recommendation scenarios, where SSM captures temporal dynamics through state compression while attention models both similar and diverse item relationships. Extensive experiments on four real-world datasets demonstrate that STAR-Rec consistently outperforms state-of-the-art sequential recommendation methods, particularly in scenarios involving diverse user behaviors and varying sequence lengths.
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