Traditional e-commerce search systems employ multi-stage cascading architectures (MCA) that progressively filter items through recall, pre-ranking, and ranking stages. While effective at balancing computational efficiency with business conversion, these systems suffer from fragmented computation and optimization objective collisions across stages, which ultimately limit their performance ceiling. To address these, we propose \textbf{OneSearch}, the first industrial-deployed end-to-end generative framework for e-commerce search. This framework introduces three key innovations: (1) a Keyword-enhanced Hierarchical Quantization Encoding (KHQE) module, to preserve both hierarchical semantics and distinctive item attributes while maintaining strong query-item relevance constraints; (2) a multi-view user behavior sequence injection strategy that constructs behavior-driven user IDs and incorporates both explicit short-term and implicit long-term sequences to model user preferences comprehensively; and (3) a Preference-Aware Reward System (PARS) featuring multi-stage supervised fine-tuning and adaptive reward-weighted ranking to capture fine-grained user preferences. Extensive offline evaluations on large-scale industry datasets demonstrate OneSearch's superior performance for high-quality recall and ranking. The rigorous online A/B tests confirm its ability to enhance relevance in the same exposure position, achieving statistically significant improvements: +1.67% item CTR, +2.40% buyer, and +3.22% order volume. Furthermore, OneSearch reduces operational expenditure by 75.40% and improves Model FLOPs Utilization from 3.26% to 27.32%. The system has been successfully deployed across multiple search scenarios in Kuaishou, serving millions of users, generating tens of millions of PVs daily.

翻译：传统的电商搜索系统采用多阶段级联架构，通过召回、粗排和精排阶段逐步筛选商品。虽然这种架构在平衡计算效率与商业转化方面效果显著，但其存在计算过程割裂及各阶段优化目标冲突的问题，最终限制了系统的性能上限。为解决这些问题，我们提出了\textbf{OneSearch}，首个实现工业部署的端到端生成式电商搜索框架。该框架引入了三项关键创新：(1) 关键词增强的层次化量化编码模块，在保持强查询-商品相关性约束的同时，保留层次化语义和独特的商品属性；(2) 多视图用户行为序列注入策略，构建行为驱动的用户ID，并融合显式短期与隐式长期序列以全面建模用户偏好；(3) 偏好感知奖励系统，采用多阶段监督微调和自适应奖励加权排序来捕捉细粒度用户偏好。在大规模行业数据集上的广泛离线评估表明，OneSearch在高质量召回和排序方面均表现出优越性能。严格的在线A/B测试证实了其在相同曝光位置下提升相关性的能力，取得了统计上显著的改进：商品点击率提升+1.67%，买家数提升+2.40%，订单量提升+3.22%。此外，OneSearch将运营支出降低了75.40%，并将模型FLOPs利用率从3.26%提升至27.32%。该系统已在快手多个搜索场景中成功部署，为数百万用户提供服务，每日产生数千万页面浏览量。