# Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning

arXiv: https://arxiv.org/abs/2602.13134 · HTML: https://mlsanity.com/paper/2602.13134/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in LLM reasoning for recommendation, one of 17 directions in LLM reasoning.
- Builds on 19 of its 49 references that are in the map.
- First posted on arXiv 13 Feb 2026.

## Placed in

- LLM reasoning / [LLM reasoning for recommendation](https://mlsanity.com/lr/llm-reasoning-for-recommendation/)

## Builds on (most cited first, 19 of 19)

- [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://mlsanity.com/paper/2403.13372/) (2024)
- [Instruction Tuning with GPT-4](https://mlsanity.com/paper/2304.03277/) (2023)
- [OneRec: Unifying Retrieve and Rank with Generative Recommender and Iterative Preference Alignment](https://mlsanity.com/paper/2502.18965/) (2025)
- [OneRec-Think: In-Text Reasoning for Generative Recommendation](https://mlsanity.com/paper/2510.11639/) (2025)
- [OneRec-V2 Technical Report](https://mlsanity.com/paper/2508.20900/) (2025)
- [Large Scale Product Graph Construction for Recommendation in E-commerce](https://mlsanity.com/paper/2010.05525/) (2020)
- [Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap](https://mlsanity.com/paper/2501.01945/) (2025)
- [RecGPT Technical Report](https://mlsanity.com/paper/2507.22879/) (2025)
- [Optimizing Generative Ranking Relevance via Reinforcement Learning in Xiaohongshu Search](https://mlsanity.com/paper/2512.00968/) (2025)
- [Large Language Model Simulator for Cold-Start Recommendation](https://mlsanity.com/paper/2402.09176/) (2024)
- [Large Reasoning Embedding Models: Towards Next-Generation Dense Retrieval Paradigm](https://mlsanity.com/paper/2510.14321/) (2025)
- [LLM-Alignment Live-Streaming Recommendation](https://mlsanity.com/paper/2504.05217/) (2025)
- [Align3GR: Unified Multi-Level Alignment for LLM-based Generative Recommendation](https://mlsanity.com/paper/2511.11255/) (2025)
- [LiDDA: Data Driven Attribution at LinkedIn](https://mlsanity.com/paper/2505.09861/) (2025)
- [Capturing User Interests from Data Streams for Continual Sequential Recommendation](https://mlsanity.com/paper/2506.07466/) (2025)
- [FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation](https://mlsanity.com/paper/2502.16924/) (2025)
- [Continual Low-Rank Adapters for LLM-based Generative Recommender Systems](https://mlsanity.com/paper/2510.25093/) (2025)
- [LLMSeR: Enhancing Sequential Recommendation via LLM-based Data Augmentation](https://mlsanity.com/paper/2503.12547/) (2025)
- [Click A, Buy B: Rethinking Conversion Attribution in E- Commerce Recommendations](https://mlsanity.com/paper/2507.15113/) (2025)

## Abstract (arXiv)

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.