RecGPT-V2 Technical Report
Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. While RecGPT-V1 successfully pioneered this paradigm by integrating LLM-based reasoning into user interest mining and item tag prediction, it suffers from four fundamental limitations: (1) computational inefficiency and cognitive redundancy across multiple reasoning routes; (2) insufficient explanation diversity in fixed-template generation; (3) limited generalization under supervised learning paradigms; and (4) simplistic outcome-focused evaluation that fails to match human standards.
  To address these challenges, we present RecGPT-V2 with four key innovations. First, a Hierarchical Multi-Agent System restructures intent reasoning through coordinated collaboration, eliminating cognitive duplication while enabling diverse intent coverage. Combined with Hybrid Representation Inference that compresses user-behavior contexts, our framework reduces GPU consumption by 60% and improves exclusive recall from 9.39% to 10.99%. Second, a Meta-Prompting framework dynamically generates contextually adaptive prompts, improving explanation diversity by +7.3%. Third, constrained reinforcement learning mitigates multi-reward conflicts, achieving +24.1% improvement in tag prediction and +13.0% in explanation acceptance. Fourth, an Agent-as-a-Judge framework decomposes assessment into multi-step reasoning, improving human preference alignment. Online A/B tests on Taobao demonstrate significant improvements: +2.98% CTR, +3.71% IPV, +2.19% TV, and +11.46% NER. RecGPT-V2 establishes both the technical feasibility and commercial viability of deploying LLM-powered intent reasoning at scale, bridging the gap between cognitive exploration and industrial utility.
694220f85d5b2dc105274859
Chao Yi
694220f85d5b2dc10527485a
Dian Chen
694220f85d5b2dc10527485b
Gaoyang Guo
admin_assigned
2025-12-17T10:13:52.444Z
645469c7363bb3aaf9ca9caf
/avatars/0a456a16c1447dd1dcd8d45b807af77c.svg
Gaoyang Guo
hairlatic
user
hairlatic
694220f85d5b2dc10527485c
Jiakai Tang
claimed_verified
2025-12-17T10:08:21.661Z
65acfb3a14e6582c30b4ce76
https://cdn-avatars.huggingface.co/v1/production/uploads/65acfb3a14e6582c30b4ce76/RhEhePggBtyM0RIIqXQen.jpeg
TangJiakai
TangJiakai5704
user
TangJiakai5704
694220f85d5b2dc10527485d
Jian Wu
694220f85d5b2dc10527485e
Jing Yu
694220f85d5b2dc10527485f
Mao Zhang
694220f85d5b2dc105274860
Wen Chen
694220f85d5b2dc105274861
Wenjun Yang
694220f85d5b2dc105274862
Yujie Luo
694220f85d5b2dc105274863
Yuning Jiang
694220f85d5b2dc105274864
Zhujin Gao
admin_assigned
2025-12-17T10:14:05.884Z
6942695b2057100e28ce0200
/avatars/48986efcbfd004c0b6eb866f9cbdd7c1.svg
Zhujin Gao
zhjgao
user
zhjgao
694220f85d5b2dc105274865
Bo Zheng
694220f85d5b2dc105274866
Binbin Cao
694220f85d5b2dc105274867
Changfa Wu
694220f85d5b2dc105274868
Dixuan Wang
694220f85d5b2dc105274869
Han Wu
694220f85d5b2dc10527486a
Haoyi Hu
694220f85d5b2dc10527486b
Kewei Zhu
694220f85d5b2dc10527486c
Lang Tian
694220f85d5b2dc10527486d
Lin Yang
694220f85d5b2dc10527486e
Qiqi Huang
694220f85d5b2dc10527486f
Siqi Yang
694220f85d5b2dc105274870
Wenbo Su
694220f85d5b2dc105274871
Xiaoxiao He
694220f85d5b2dc105274872
Xin Tong
694220f85d5b2dc105274873
Xu Chen
694220f85d5b2dc105274874
Xunke Xi
694220f85d5b2dc105274875
Xiaowei Huang
694220f85d5b2dc105274876
Yaxuan Wu
694220f85d5b2dc105274877
Yeqiu Yang
694220f85d5b2dc105274878
Yi Hu
694220f85d5b2dc105274879
Yujin Yuan
694220f85d5b2dc10527487a
Yuliang Yan
694220f85d5b2dc10527487b
Zile Zhou
RecGPT-V2 Technical Report
Large language models (LLMs) have demonstrated remarkable potential in transforming recommender systems from implicit behavioral pattern matching to explicit intent reasoning. While RecGPT-V1 successfully pioneered this paradigm by integrating LLM-based reasoning into user interest mining and item tag prediction, it suffers from four fundamental limitations: (1) computational inefficiency and cognitive redundancy across multiple reasoning routes; (2) insufficient explanation diversity in fixed-template generation; (3) limited generalization under supervised learning paradigms; and (4) simplistic outcome-focused evaluation that fails to match human standards.
  To address these challenges, we present RecGPT-V2 with four key innovations. First, a Hierarchical Multi-Agent System restructures intent reasoning through coordinated collaboration, eliminating cognitive duplication while enabling diverse intent coverage. Combined with Hybrid Representation Inference that compresses user-behavior contexts, our framework reduces GPU consumption by 60% and improves exclusive recall from 9.39% to 10.99%. Second, a Meta-Prompting framework dynamically generates contextually adaptive prompts, improving explanation diversity by +7.3%. Third, constrained reinforcement learning mitigates multi-reward conflicts, achieving +24.1% improvement in tag prediction and +13.0% in explanation acceptance. Fourth, an Agent-as-a-Judge framework decomposes assessment into multi-step reasoning, improving human preference alignment. Online A/B tests on Taobao demonstrate significant improvements: +2.98% CTR, +3.71% IPV, +2.19% TV, and +11.46% NER. RecGPT-V2 establishes both the technical feasibility and commercial viability of deploying LLM-powered intent reasoning at scale, bridging the gap between cognitive exploration and industrial utility.
RecGPT-V2 enhances recommender systems by integrating a Hierarchical Multi-Agent System, Hybrid Representation Inference, Meta-Prompting, constrained reinforcement learning, and an Agent-as-a-Judge framework to improve efficiency, explanation diversity, generalization, and human preference alignment.
Hierarchical Multi-Agent System Hybrid Representation Inference Meta-Prompting constrained reinforcement learning Agent-as-a-Judge intent reasoning cognitive duplication explanation diversity tag prediction explanation acceptance CTR IPV TV NER