发表机构
Google LLC（谷歌公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对工业级异构推荐信息流排序难题，提出HA-MoE架构与LENS框架，采用DL-AUC评估，离线与在线实验均证实其能有效管理推荐系统的异构性。
AI 中文摘要
异构推荐信息流面临超出高度同质环境（如仅音乐或仅视频的封闭生态平台）的复杂挑战。在Google Discover中，统一信息流整合了来自分散开放网络的多样内容，包括网页文章、长短视频、用户生成内容（UGC）等。不同内容类型呈现出截然不同的特征密度和用户交互模式。构建在这种异构性下保持高性能、同时避免负迁移或多数偏差的统一排序模型，仍是一项重大工业挑战。本文基于实际部署，针对工业级异构信息流的多任务排序开展端到端案例研究。我们提出HA-MoE，一种异构性自适应多门控混合专家架构，将显式异构性上下文融入门控网络和专家表示，该方法可实现有效专业化且不会显著增加运营开销。为支持可靠部署，我们引入LENS，一种轻量级可观测性框架，提供专家专业化的可解释诊断并跟踪持续重训练过程中的功能异构性。我们采用Dual-Level AUC（DL-AUC，一种结合全局排序性能与跨片段排序正确性的异构性感知评估指标）评估方法，对大规模工业数据集的离线评估显示其较基线模型实现持续改进，在线A/B测试也证实信息流活跃度与探索指标的提升。离线与在线结果共同验证了该方法在管理工业级推荐系统异构性方面的有效性。
英文摘要
Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.g., music-only or video-only closed-ecosystem platforms). In Google Discover, a unified feed integrates diverse content sourced from the decentralized open web, including web articles, long-form and short-form videos, user-generated content (UGC), and beyond. Different content types exhibit distinct feature densities and user interaction patterns. Building a unified ranking model that sustains high performance across such heterogeneity, while avoiding negative transfer or majority bias, remains a significant industrial challenge. This paper presents an end-to-end case study on the industrial-scale multi-task ranking of heterogeneous feeds, grounded in real-world deployment. We introduce HA-MoE, a heterogeneity-adaptive multi-gated mixture-of-experts architecture that incorporates explicit heterogeneity context into both gating networks and expert representations. This approach enables effective specialization without significantly increasing operational overhead. To support reliable deployment, we introduce LENS, a lightweight observability framework that provides interpretable diagnostics of expert specialization and tracks this functional heterogeneity across continuous retraining. We evaluate our method using Dual-Level AUC (DL-AUC), a heterogeneity-aware evaluation metric that combines global ranking performance with cross-segment ranking correctness. Offline evaluations on a large-scale industrial dataset demonstrate consistent improvements over baseline models. Furthermore, online A/B testing confirms gains in feed activity and exploration metrics. Together, offline and online results validate the effectiveness of our approach for managing heterogeneity in industrial-scale recommender systems.
CommentsAccepted to ACM RecSys Industry Track 2026
Journal refProceedings of ACM RecSys 2026
