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

arXiv: https://arxiv.org/abs/2608.18606 · HTML: https://mlsanity.com/paper/2608.18606/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2026.

## Built on by (most cited first, 1 of 1)

- [AtomRec: Evolving Atomic Memory for Agentic Recommendation](https://mlsanity.com/paper/2609.04882/) (2026)

## Abstract (arXiv)

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