arXiv cs.LG· Hua Zheng, Shali Jiang, Boyang Liu, Laming Chen, Kenny Lov, Chuanqi Xu, Lisang Ding, Qinghai Zhou, Can Cui, Xiaolong Liu, Xiaoyi Liu, Yasmine Badr, Xin Xu, Mingfu Liang, Jiyan Yang, Ellie Dingqiao Wen, Gerard Jonathan Mugisha Akkerhuis, Jason Rudy, Xi Liu, Chenxiao Guan, Rong Jin, Ruichao Qiu, Xian Chen, Zhehui Zhou, Ping Chen, Rui Yang, Haicheng Chen, Meet Raval, Song Zhou, Dharak Kharod, Shuyu Xu, Xingyuan Wang, Liang Tao, Qiang Jin, Qiao Yang, Wankun Zhu, Qin Huang, Yuzhen Huang, Darren Liu, Parish Aggarwal, Hui Zhou, Erzhuo Wang, Shuo Chang, Xiaorui Gan, Wenlin Chen, Santanu Kolay, Huayu Li·· 6 小时前AI 评分24
LoopFM：用基础模型历史表示提升推荐模型知识蒸馏
LoopFM: Learning frOm HistOrical RePresentations of Foundation Model for Recommendation
AI 导读
针对知识蒸馏仅传递单一标量预测、迁移比递减的瓶颈，LoopFM 将基础模型（FM）的中间嵌入结构化为下游垂直模型（VM）的输入特征（如用户历史序列），无需服务时实时 FM 推理，也无需 FM 与 VM 架构耦合。
来源：arXiv cs.LG · arxiv.org