Predicting user preferences and sequential dependencies based on historical behavior is the core goal of sequential recommendation. Although attention-based models have shown effectiveness in this field, they often struggle with inference inefficiency due to the quadratic computational complexity inherent in attention mechanisms, especially with long-range behavior sequences. Drawing inspiration from the recent advancements of state space models (SSMs) in control theory, which provide a robust framework for modeling and controlling dynamic systems, we introduce EchoMamba4Rec. Control theory emphasizes the use of SSMs for managing long-range dependencies and maintaining inferential efficiency through structured state matrices. EchoMamba4Rec leverages these control relationships in sequential recommendation and integrates bi-directional processing with frequency-domain filtering to capture complex patterns and dependencies in user interaction data more effectively. Our model benefits from the ability of state space models (SSMs) to learn and perform parallel computations, significantly enhancing computational efficiency and scalability. It features a bi-directional Mamba module that incorporates both forward and reverse Mamba components, leveraging information from both past and future interactions. Additionally, a filter layer operates in the frequency domain using learnable Fast Fourier Transform (FFT) and learnable filters, followed by an inverse FFT to refine item embeddings and reduce noise. We also integrate Gate Linear Units (GLU) to dynamically control information flow, enhancing the model's expressiveness and training stability. Experimental results demonstrate that EchoMamba significantly outperforms existing models, providing more accurate and personalized recommendations.

翻译：基于历史行为预测用户偏好和序列依赖性是序列推荐的核心目标。尽管基于注意力机制的模型在该领域已显示出有效性，但由于注意力机制固有的二次计算复杂度，尤其是在处理长范围行为序列时，它们常常面临推理效率低下的问题。受控制理论中状态空间模型（SSMs）最新进展的启发——该模型为动态系统的建模与控制提供了稳健框架——我们提出了EchoMamba4Rec。控制理论强调利用SSMs管理长程依赖关系，并通过结构化状态矩阵保持推理效率。EchoMamba4Rec在序列推荐中利用这些控制关系，并整合双向处理与频域滤波，以更有效地捕捉用户交互数据中的复杂模式和依赖关系。我们的模型受益于状态空间模型（SSMs）学习和执行并行计算的能力，显著提升了计算效率和可扩展性。其核心是一个双向Mamba模块，包含前向和反向Mamba组件，充分利用过去和未来交互的信息。此外，一个滤波层在频域中运行，使用可学习的快速傅里叶变换（FFT）和可学习滤波器，随后进行逆FFT以优化项目嵌入并降低噪声。我们还集成了门控线性单元（GLU）来动态控制信息流，从而增强模型的表达能力和训练稳定性。实验结果表明，EchoMamba显著优于现有模型，能够提供更准确和个性化的推荐。