UNIQUE：面向大规模信息流推荐系统的统一检索与排序框架
UNIQUE: A Unified Retrieval and Ranking System for Large-Scale Feed Recommendation
- Beihang University（北京航空航天大学）
- Baidu Inc.（百度公司）
- Hong Kong Institute of AI for Science, City University of Hong Kong（香港人工智能科学研究所，香港城市大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对工业信息流推荐中检索排序分离及量化不稳定问题，提出统一框架UNIQUE，采用单层扁平量化与早期融合架构，在百度多场景部署，显著提升观看时长与分发量。
AI中文摘要：
工业移动信息流系统依赖检索-排序流水线，在严格的延迟约束下服务于大规模、异构且快速变化的内容。然而，现有流水线仍存在两个关键问题：候选检索中的分层量化不稳定，以及分离的检索与排序阶段之间的信息损失。这些问题损害了长尾和冷启动推荐，并使高效服务复杂化。为解决这些问题，我们提出了UNIQUE，一个采用单层扁平量化的统一检索与排序推荐框架。UNIQUE将基于生成式编码的检索和目标感知的排序集成到一个早期融合架构中，在共享表示下实现端到端训练，同时保持高效的候选生成。进一步引入了均衡量化机制以缓解码本不平衡并改善长尾表示。离线实验从检索和排序两个角度评估了UNIQUE，而码本分析显示其资源分配比分层量化更均衡。我们在百度移动端的主页信息流、发现页和短视频推荐场景中部署了UNIQUE，服务于大规模真实流量。在线A/B测试在总观看时长上实现了0.96%的提升，在总分发量上实现了1.08%的提升，其中新用户和高活跃用户的改进尤为显著。服务测量显示P99延迟为89毫秒，在线推理MFU为44.23%。这些结果表明，UNIQUE为工业推荐中的统一检索与排序提供了一个稳定、高效且可投入生产的框架。
英文摘要：
Industrial mobile feed systems rely on a retrieval-ranking pipeline to serve large-scale, heterogeneous, and fast-changing content under strict latency constraints. However, existing pipelines still suffer from two critical issues: hierarchical quantization instability in candidate retrieval and information loss between separated retrieval and ranking stages. These issues hurt long-tail and cold-start recommendation and complicate efficient serving. To address them, we present UNIQUE, a unified retrieval and ranking recommendation framework with single-layer flat quantization. UNIQUE integrates generative code-based retrieval and target-aware ranking into one early-fusion architecture, enabling end-to-end training under a shared representation while preserving efficient candidate generation. A balanced quantization mechanism is further introduced to mitigate codebook imbalance and improve long-tail representation. Offline experiments evaluate UNIQUE from both retrieval and ranking perspectives, while codebook analysis shows more balanced resource allocation than hierarchical quantization. We deploy UNIQUE in the homepage feed, discovery-page, and short-video recommendation scenarios of Mobile Baidu, serving large-scale real-world traffic. Online A/B tests achieve a 0.96% gain in total watch duration and a 1.08% gain in total distribution volume, with notable improvements for new users and highly active users. Serving measurements show 89 ms P99 latency and 44.23% online inference MFU. These results show that UNIQUE provides a stable, efficient, and production-ready framework for unified retrieval and ranking in industrial recommendation.
