OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Ranking

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 $\mathbf{\text{OneModel}}$, 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 $\mathbf{\text{+0.33\%}}$ and Engagement by $\mathbf{\text{+1.25\%}}$ in Explore Feed, lift advertising value by $\mathbf{\text{+3.43\%}}$ and CTR by $\mathbf{\text{+8.18\%}}$ in Feed Advertising, and raise DGMV by $\mathbf{\text{+1.1867\%}}$ and GPM by $\mathbf{\text{+2.1585\%}}$ in Merchant Recommendation, validating unified multi-stream ranking as an effective production foundation.