Industrial-scale recommender systems rely on a cascade pipeline in which the retrieval stage must return a high-recall candidate set from billions of items under tight latency. Existing solutions either (i) suffer from limited expressiveness in capturing fine-grained user-item interactions, as seen in decoupled dual-tower architectures that rely on separate encoders, or generative models that lack precise target-aware matching capabilities, or (ii) build structured indices (tree, graph, quantization) whose item-centric topologies struggle to incorporate dynamic user preferences and incur prohibitive construction and maintenance costs. We present GRank, a novel structured-index-free retrieval paradigm that seamlessly unifies target-aware learning with user-centric retrieval. Our key innovations include: (1) A target-aware Generator trained to perform personalized candidate generation via GPU-accelerated MIPS, eliminating semantic drift and maintenance costs of structured indexing; (2) A lightweight but powerful Ranker that performs fine-grained, candidate-specific inference on small subsets; (3) An end-to-end multi-task learning framework that ensures semantic consistency between generation and ranking objectives. Extensive experiments on two public benchmarks and a billion-item production corpus demonstrate that GRank improves Recall@500 by over 30% and 1.7$\times$ the P99 QPS of state-of-the-art tree- and graph-based retrievers. GRank has been fully deployed in production in our recommendation platform since Q2 2025, serving 400 million active users with 99.95% service availability. Online A/B tests confirm significant improvements in core engagement metrics, with Total App Usage Time increasing by 0.160% in the main app and 0.165% in the Lite version.

翻译：工业级推荐系统依赖于级联流水线，其中检索阶段必须在严格延迟限制下从数十亿物品中返回高召回率候选集。现有方案存在以下问题：(i) 双塔解耦架构依赖独立编码器导致细粒度用户-物品交互建模能力有限，或生成式模型缺乏精准的目标感知匹配能力；(ii) 结构化索引（树、图、量化）虽能构建物品中心拓扑结构，但难以融入动态用户偏好且构建维护成本高昂。我们提出GRank——一种无需结构化索引的全新检索范式，将目标感知学习与用户中心检索无缝统一。核心创新包括：(1) 目标感知生成器，通过GPU加速的MIPS实现个性化候选生成，消除语义漂移及结构化索引维护成本；(2) 轻量化排序器，对小规模候选集执行细粒度的特征级推理；(3) 端到端多任务学习框架，确保生成与排序目标的语义一致性。在两个公开基准数据集及十亿级生产语料库上的实验表明，GRank将Recall@500提升超30%，P99 QPS达现有树型/图型检索器的1.7倍。自2025年第二季度起，GRank已全面部署于我们推荐平台的生产环境，为4亿活跃用户提供99.95%的服务可用性。在线A/B测试证实核心参与指标显著提升：主应用与精简版总使用时长分别增加0.160%和0.165%。