Modeling long-term user behavior trajectories is essential for understanding evolving preferences and enabling proactive recommendations. However, most sequential recommenders focus on next-item prediction, overlooking dependencies across multiple future actions. We propose Generative Chain of Behavior (GCB), a generative framework that models user interactions as an autoregressive chain of semantic behaviors over multiple future steps. GCB first encodes items into semantic IDs via RQ-VAE with k-means refinement, forming a discrete latent space that preserves semantic proximity. On top of this space, a transformer-based autoregressive generator predicts multi-step future behaviors conditioned on user history, capturing long-horizon intent transitions and generating coherent trajectories. Experiments on benchmark datasets show that GCB consistently outperforms state-of-the-art sequential recommenders in multi-step accuracy and trajectory consistency. Beyond these gains, GCB offers a unified generative formulation for capturing user preference evolution.

翻译：对用户长期行为轨迹进行建模对于理解其偏好演变和实现前瞻性推荐至关重要。然而，大多数序列推荐模型聚焦于下一项预测，忽视了多个未来动作之间的依赖关系。本文提出生成行为链（Generative Chain of Behavior, GCB），这是一个生成式框架，将用户交互建模为跨越多个未来步骤的语义行为自回归链。GCB首先通过结合k均值精化的RQ-VAE将物品编码为语义ID，形成一个保持语义邻近性的离散潜在空间。在此空间之上，一个基于Transformer的自回归生成器以用户历史为条件预测多步未来行为，从而捕捉长时程意图转移并生成连贯的轨迹。在基准数据集上的实验表明，GCB在多步预测准确性和轨迹连贯性方面持续优于最先进的序列推荐模型。除了这些性能提升，GCB为捕捉用户偏好演变提供了一个统一的生成式建模框架。