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English(EN) OneModel: A Unified Foundation for Platform-Scale Multi-Scenario Ranking

OneModel 统一平台排序系统,提升用户参与度和广告效果

研究人员推出 OneModel,这是一个统一的框架,旨在整合像小红书这样的大型平台内的多个排序系统。该方法旨在通过将多样化的用户行为映射到共享序列来改进用户表示并降低工程成本。该框架包含场景感知调制,用于平衡跨流和流内学习,并针对生产部署和在线服务进行了优化。初步部署已显示用户参与度和广告指标显著提升。 AI

影响 这种统一的排序系统方法可以简化开发并改善跨不同平台功能的 istio 用户体验。

排序理由 该集群包含一篇详细介绍信息检索系统新框架的学术论文。

在 arXiv cs.IR (Information Retrieval) 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

OneModel 统一平台排序系统,提升用户参与度和广告效果

本文如何被排名

Signal score
0 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Research
该集群包含一篇详细介绍信息检索系统新框架的学术论文。
Source corroboration
2 independent sources
Multiple independent publishers reporting the same story raises confidence that it's real and newsworthy.
Topics
paper, product, infra
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
6 days old
Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

完整方法见我们的编辑标准

报道来源 [2]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yao Hu ·

    OneModel:平台级多场景排名的统一基础

    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 incr…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Yao Hu ·

    OneModel:平台级多场景排名的统一基础

    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 incr…