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

2026-08-19

AI Summary

本文提出OneModel框架，通过统一多场景排序解决平台级推荐系统中用户行为跨流连续性问题，采用共享事件序列和情景感知信息调节等方法增强用户表示。

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.

Problem

• recommender systems
• multi-stream ranking
• user representation
• cross-stream transfer
• platform-scale

Innovation

• Unified Framework
• Scenario-aware Information Modulation
• Long-context User Representations
• Multi-objective Training
• Online Serving Optimization