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
Published: 2025-12-16 15:40:44

Abstract: 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.