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

• URL: http://arxiv.org/abs/2608.18606v2
• Date: Thu, 20 Aug 2026 02:58:30 GMT
• Title: OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Ranking
• Abstract summary: We propose textbfOneModel, a unified framework for multi-stream final ranking.<n>OneModel maps heterogeneous behaviors into shared event sequences, learns long-context user representations with an action-oriented backbone.<n>We deploy OneModel in production at emphXiaohongshu, where it delivers consistent offline gains over strong baselines.
• Score: 11.905656321952394
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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