English(EN) LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

LoopFM框架增强了基础模型向推荐系统迁移知识的能力

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-28 02:59

研究人员开发了LoopFM，一个旨在改善从大型基础模型（FM）向小型垂直模型（VM）迁移知识的新框架。与传统的知识蒸馏不同，LoopFM将FM的中间嵌入结构化为VM的输入特征，创建了一个更高带宽的迁移通道。这种方法避免了实时FM推理和架构耦合，在基准测试和工业规模系统上取得了显著的性能提升，包括转化率的显著提高。 AI

影响 LoopFM的方法可以通过实现从大型基础模型到小型、专业化模型更好的知识迁移，从而显著提高推荐系统的效率和有效性。

排序理由该集群包含一篇详细介绍机器学习新框架的学术论文。

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 →

报道来源 [2]

arXiv cs.AI TIER_1 English(EN) · Shali Jiang, Hua Zheng, Boyang Liu, Laming Chen, Kenny Lov, Chuanqi Xu, Lisang Ding, Qinghai Zhou, Can Cui, Xiaolong Liu, Xiaoyi Liu, Yasmine Badr, Xin Xu, Jiyan Yang, Ellie Dingqiao Wen, Gerard Jonathan Mugisha Akkerhuis, Chenxiao Guan, Rong Jin, Ruicha… · 2026-05-29 04:00

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

arXiv:2605.29280v1 Announce Type: cross Abstract: Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- the fraction of FM improvement captured by the VM -…
arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Huayu Li · 2026-05-28 02:59

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

Knowledge distillation (KD) transfers a single scalar prediction from a large foundation model (FM) to compact vertical models (VMs), suffering from diminishing transfer ratio -- the fraction of FM improvement captured by the VM -- as a single scalar cannot convey the rich interm…

报道来源 [2]

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation

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