Awakening dormant users, who remain engaged but exhibit low conversion, is a pivotal driver for incremental GMV growth in large-scale e-commerce platforms. However, existing approaches often yield suboptimal results since they typically rely on single-step estimation of an item's intrinsic value (e.g., immediate click probability). This mechanism overlooks the instrumental effect of items, where specific interactions act as triggers to shape latent intent and drive subsequent decisions along a conversion trajectory. To bridge this gap, we propose RoleGen, a novel framework that synergizes a Conversion Trajectory Reasoner with a Generative Behavioral Backbone. Specifically, the LLM-based Reasoner explicitly models the context-dependent Functional Role of items to reconstruct intent evolution. It further employs counterfactual inference to simulate diverse conversion paths, effectively mitigating interest collapse. These reasoned candidate items are integrated into the generative backbone, which is optimized via a collaborative "Reasoning-Execution-Feedback-Reflection" closed-loop strategy to ensure grounded execution. Extensive offline experiments and online A/B testing on the Kuaishou e-commerce platform demonstrate that RoleGen achieves a 6.2% gain in Recall@1 and a 7.3% increase in online order volume, confirming its effectiveness in activating the dormant user base.

翻译：唤醒沉睡用户——即保持活跃但转化率较低的群体——是大型电商平台实现增量GMV增长的关键驱动力。然而，现有方法通常依赖于对商品内在价值（如即时点击概率）的单步估计，往往导致次优结果。这种机制忽视了商品的工具性效应，即特定交互行为可作为触发器，塑造潜在意图并推动用户沿转化轨迹做出后续决策。为弥补这一不足，我们提出RoleGen框架，该框架将转化轨迹推理器与生成式行为主干网络协同整合。具体而言，基于LLM的推理器显式建模商品在上下文依赖中的功能角色，以重构意图演化过程。该框架进一步采用反事实推理模拟多样化转化路径，有效缓解兴趣坍缩问题。经推理生成的候选商品被整合至生成式主干网络中，并通过协同的“推理-执行-反馈-反思”闭环策略进行优化，以确保落地执行的可靠性。在快手电商平台开展的广泛离线实验与在线A/B测试表明，RoleGen在Recall@1指标上实现6.2%的提升，并带来7.3%的在线订单量增长，证实了其在激活沉睡用户群体方面的有效性。