@inproceedings{luo-etal-2026-awakening,
title = "Awakening Dormant Users: Modeling Intent Evolution via {LLM}-based Functional Role Reasoning for Generative Recommendation",
author = "Luo, Huishi and
Li, Shuokai and
Yang, Hanchen and
Sun, Zhongbo and
Ding, Haojie and
Zhang, Boheng and
Cai, Zijia and
Qian, Renliang and
Yang, Fan and
Gao, Tingting and
Lei, Chenyi and
Ou, Wenwu and
Zhuang, Fuzhen",
editor = "Atanasova, Pepa and
Haffari, Gholamreza and
Wang, Minghan",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-industry.199/",
pages = "3334--3356",
ISBN = "979-8-89176-460-6",
abstract = "Awakening dormant users, who remain engaged but exhibit low conversion, is a key driver for incremental GMV growth in e-commerce platforms. Existing methods often underperform since they typically rely on single-step estimation of an item{'}s intrinsic value (e.g., immediate click probability). This 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 framework that synergizes a Conversion Trajectory Reasoner with a Generative Collaborative Executor. Specifically, the LLM-based Reasoner explicitly models the context-dependent Functional Role of items to reconstruct intent evolution. It further uses counterfactual role exploration to mitigate interest collapse and generate diverse conversion-oriented candidates. These reasoned candidate items are then injected into the Executor, which is optimized via a feedback-driven co-training mechanism to ensure grounded execution. Extensive offline experiments and online A/B testing on a large-scale e-commerce platform show a 5.82{\%} gain in Recall@1 and a 6.71{\%} increase in online order volume, confirming RoleGen{'}s effectiveness in activating dormant users."
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<abstract>Awakening dormant users, who remain engaged but exhibit low conversion, is a key driver for incremental GMV growth in e-commerce platforms. Existing methods often underperform since they typically rely on single-step estimation of an item’s intrinsic value (e.g., immediate click probability). This 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 framework that synergizes a Conversion Trajectory Reasoner with a Generative Collaborative Executor. Specifically, the LLM-based Reasoner explicitly models the context-dependent Functional Role of items to reconstruct intent evolution. It further uses counterfactual role exploration to mitigate interest collapse and generate diverse conversion-oriented candidates. These reasoned candidate items are then injected into the Executor, which is optimized via a feedback-driven co-training mechanism to ensure grounded execution. Extensive offline experiments and online A/B testing on a large-scale e-commerce platform show a 5.82% gain in Recall@1 and a 6.71% increase in online order volume, confirming RoleGen’s effectiveness in activating dormant users.</abstract>
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%0 Conference Proceedings
%T Awakening Dormant Users: Modeling Intent Evolution via LLM-based Functional Role Reasoning for Generative Recommendation
%A Luo, Huishi
%A Li, Shuokai
%A Yang, Hanchen
%A Sun, Zhongbo
%A Ding, Haojie
%A Zhang, Boheng
%A Cai, Zijia
%A Qian, Renliang
%A Yang, Fan
%A Gao, Tingting
%A Lei, Chenyi
%A Ou, Wenwu
%A Zhuang, Fuzhen
%Y Atanasova, Pepa
%Y Haffari, Gholamreza
%Y Wang, Minghan
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-460-6
%F luo-etal-2026-awakening
%X Awakening dormant users, who remain engaged but exhibit low conversion, is a key driver for incremental GMV growth in e-commerce platforms. Existing methods often underperform since they typically rely on single-step estimation of an item’s intrinsic value (e.g., immediate click probability). This 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 framework that synergizes a Conversion Trajectory Reasoner with a Generative Collaborative Executor. Specifically, the LLM-based Reasoner explicitly models the context-dependent Functional Role of items to reconstruct intent evolution. It further uses counterfactual role exploration to mitigate interest collapse and generate diverse conversion-oriented candidates. These reasoned candidate items are then injected into the Executor, which is optimized via a feedback-driven co-training mechanism to ensure grounded execution. Extensive offline experiments and online A/B testing on a large-scale e-commerce platform show a 5.82% gain in Recall@1 and a 6.71% increase in online order volume, confirming RoleGen’s effectiveness in activating dormant users.
%U https://aclanthology.org/2026.emnlp-industry.199/
%P 3334-3356
Markdown (Informal)
[Awakening Dormant Users: Modeling Intent Evolution via LLM-based Functional Role Reasoning for Generative Recommendation](https://aclanthology.org/2026.emnlp-industry.199/) (Luo et al., EMNLP 2026)
ACL
- Huishi Luo, Shuokai Li, Hanchen Yang, Zhongbo Sun, Haojie Ding, Boheng Zhang, Zijia Cai, Renliang Qian, Fan Yang, Tingting Gao, Chenyi Lei, Wenwu Ou, and Fuzhen Zhuang. 2026. Awakening Dormant Users: Modeling Intent Evolution via LLM-based Functional Role Reasoning for Generative Recommendation. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing: Industry Track, pages 3334–3356, Budapest, Hungary. Association for Computational Linguistics.