RecGPT-V2 Technical Report
Authors: name: Chao Yi, name: Dian Chen, name: Gaoyang Guo, name: Jiakai Tang, name: Jian Wu, name: Jing Yu, name: Mao Zhang, name: Wen Chen, name: Wenjun Yang, name: Yujie Luo, name: Yuning Jiang, name: Zhujin Gao, name: Bo Zheng, name: Binbin Cao, name: Changfa Wu, name: Dixuan Wang, name: Han Wu, name: Haoyi Hu, name: Kewei Zhu, name: Lang Tian, name: Lin Yang, name: Qiqi Huang, name: Siqi Yang, name: Wenbo Su, name: Xiaoxiao He, name: Xin Tong, name: Xu Chen, name: Xunke Xi, name: Xiaowei Huang, name: Yaxuan Wu, name: Yeqiu Yang, name: Yi Hu, name: Yujin Yuan, name: Yuliang Yan, name: Zile Zhou

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.