Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning

📅 2026-02-13
📈 Citations: 0
Influential: 0
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📝 Abstract
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.
Problem

Research questions and friction points this paper is trying to address.

dormant users
conversion trajectory
functional role
counterfactual reasoning
generative recommendation
Innovation

Methods, ideas, or system contributions that make the work stand out.

Functional Role Reasoning
Counterfactual Inference
Generative Recommendation
Conversion Trajectory Modeling
Dormant User Activation
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