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English(EN) Kunlun: Establishing Scaling Laws for Massive-Scale Recommendation Systems through Unified Architecture Design

Meta Ads 采用昆仑架构以提高推荐系统效率

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了昆仑，一种旨在提高推荐系统效率和可扩展性的新架构。通过结合广义点积注意力（Generalized Dot-Product Attention）和计算跳过（Computation Skip）等优化，昆仑将推荐模型的扩展效率提高到现有方法的两倍。该架构已部署在 Meta Ads 模型中，显示出显著的生产影响。 AI

影响 提高了大规模推荐系统的效率，可能改善用户体验和广告定位效果。

排序理由 详细介绍新架构及其性能改进的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Meta Ads 采用昆仑架构以提高推荐系统效率

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详细介绍新架构及其性能改进的研究论文。[lever_c_demoted from research: ic=1 ai=0.7]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Bojian Hou, Xiaolong Liu, Xiaoyi Liu, Jiaqi Xu, Yasmine Badr, Mengyue Hang, Sudhanshu Chanpuriya, Junqing Zhou, Yuhang Yang, Han Xu, Qiuling Suo, Laming Chen, Yuxi Hu, Jiasheng Zhang, Huaqing Xiong, Yuzhen Huang, Chao Chen, Yue Dong, Yi Yang, Shuo Chang,… ·

    昆仑：通过统一架构设计为大规模推荐系统建立扩展定律

    arXiv:2602.10016v3 Announce Type: replace-cross Abstract: Deriving predictable scaling laws that govern the relationship between model performance and computational investment is crucial for designing and allocating resources in massive-scale recommendation systems. While such la…

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