用于序列推荐的哈密顿光谱时空耗散动力学
Hamiltonian Spectral-Temporal Dissipative Dynamics for Sequential Recommendation
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中文总结 AI 辅助
该研究针对现有序列推荐模型难以捕捉用户丰富动态行为的问题，提出哈密顿光谱推荐器HSR，通过二阶耗散哈密顿系统建模偏好演变，在多基准数据集上优于同类先进模型。
中文摘要 AI 辅助
序列推荐需要理解用户偏好如何随时间演变，但现有大多数模型将这种演变视为一阶过程，即下一状态仅取决于当前隐式表示。然而，真实用户行为常表现出更丰富的动态特性，包括惯性、周期性和突然转变，这些无法被一阶假设完全捕捉。受这些行为特征驱动，我们通过二阶动力学系统的视角重新构建序列推荐，并引入哈密顿光谱推荐器（Hamiltonian Spectral Recommender, HSR），将偏好演变重新表述为隐式相空间中位置（稳定偏好）和动量（短期倾向）的耗散哈密顿系统。控制方程的线性时不变结构在频域中存在闭式解。可学习的耗散机制进一步捕捉自然兴趣衰减，而短局部脉冲细化模块则对稀疏交互日志中常见的突发行为波动进行建模。该设计同时考虑了现有序列模型中代表性不足的三种现象：全局周期性模式、惯性演变和局部冲击。在三个基准数据集上进行的大量实验表明，HSR始终优于最先进的基于Transformer和基于状态空间模型（state space model, SSM）的推荐器。
英文摘要
Sequential recommendation requires understanding how user preferences evolve over time, yet most existing models treat such evolution as a first order process where the next state depends solely on the current latent representation. Nevertheless, real user behavior often exhibits richer dynamics, including inertia, periodicity, and sudden shifts that cannot be fully captured by these first order assumptions. Motivated by these behavioral characteristics, we reconceptualize sequential recommendation through the lens of second order dynamical systems and introduce the Hamiltonian Spectral Recommender (HSR), which recasts preference evolution as a dissipative Hamiltonian system in a latent phase space of position (stable preference) and momentum (short-term tendency). The linear time-invariant structure of the governing equation admits a closed-form solution in the frequency domain. A learnable dissipation mechanism further captures natural interest decay, while a short local impulse refinement module models abrupt behavioral fluctuations commonly observed in sparse interaction logs. This design jointly accounts for global periodic patterns, inertial evolution, and localized shocks, where three phenomena that are underrepresented in existing sequential models. Extensive experiments on three benchmark datasets demonstrate that HSR consistently outperforms state-of-the-art Transformer-based and state space model (SSM)-based recommenders.
