OneModel：面向平台级多场景排序的统一基础框架
OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Ranking
AI总结：
该研究针对平台级多业务流推荐系统的割裂问题，提出OneModel统一框架，在小红书部署后多场景指标均获提升，验证了统一多流排序的有效性。
AI中文摘要：
平台级推荐系统通常覆盖自然推荐、广告、商家服务等多个业务流，用户行为形成连续的跨流轨迹。维护独立的排序系统会割裂用户表征并增加工程成本。我们提出OneModel，这是一种面向多流最终排序的统一框架。OneModel将异构行为映射为共享事件序列，通过面向动作的主干网络学习长上下文用户表征，并引入场景感知信息调制来平衡跨流迁移与流特定专业化。为支持生产部署，OneModel进一步采用分层用户表征、多目标训练，以及通过特征分解、用户特征预取、共享用户塔计算和图级推理优化实现的优化在线服务。我们在小红书（Xiaohongshu）部署了OneModel，其在离线测试中持续优于强基线，且随上下文长度和模型容量呈良好扩展性。在线A/B测试显示，其在探索流提升停留时长0.33%、互动量1.25%；在信息流广告提升广告价值3.43%、点击率8.18%；在商家推荐提升GMV（DGMV）1.1867%、每千次展示收入（GPM）2.1585%，验证了统一多流排序是有效的生产基础。
英文摘要：
Platform-scale recommender systems often span multiple business streams such as organic recommendation, advertising, and merchant services, where user behaviors form a continuous cross-stream trajectory. Maintaining separate ranking systems fragments user representations and increases engineering cost. We propose \textbf{OneModel}, a unified framework for multi-stream final ranking. OneModel maps heterogeneous behaviors into shared event sequences, learns long-context user representations with an action-oriented backbone, and introduces \emph{Scenario-aware Information Modulation} to balance cross-stream transfer and stream-specific specialization. For production deployment, OneModel further adopts stratified user representation, multi-objective training, and optimized online serving with feature decomposition, user feature prefetching, shared user-tower computation, and graph-level inference optimization. We deploy OneModel in production at \emph{Xiaohongshu}, where it delivers consistent offline gains over strong baselines and scales favorably with context length and model capacity. Online A/B tests improve Time Spent by \textbf{+0.33\%} and Engagement by \textbf{+1.25\%} in Explore Feed, lift advertising value by \textbf{+3.43\%} and CTR by \textbf{+8.18\%} in Feed Advertising, and raise DGMV by \textbf{+1.1867\%} and GPM by \textbf{+2.1585\%} in Merchant Recommendation, validating unified multi-stream ranking as an effective production foundation.
