用生成式链路预测用户多步未来行为，更懂长期偏好变化。
Generative Chain of Behavior for User Trajectory Prediction
- 将用户行为转为语义编码，构建可生成的离散潜空间。
- 在多个未来步骤上预测行为，准确率和连贯性均领先。
- 适合需要理解用户长期兴趣演化的推荐系统场景。
建模用户的长期行为轨迹对于理解偏好演变和实现主动推荐至关重要。然而，多数序列推荐方法仅关注下一步预测，忽略了多个未来动作间的依赖关系。本文提出生成式行为链（GCB），一个将用户交互建模为多步未来行为自回归链的生成框架。GCB首先通过带k-means优化的RQ-VAE将物品编码为语义ID，形成保持语义相近性的离散潜空间；在此空间上，基于Transformer的自回归生成器根据用户历史预测多步未来行为，捕捉长程意图转移并生成连贯轨迹。在基准数据集上的实验表明，GCB在多步预测准确率和轨迹一致性方面持续优于现有先进方法。此外，该框架提供了统一的生成范式，用于捕捉用户偏好的演化过程。
原文摘要 · Abstract (English)
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.
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