一个联合推系统,具有双层多头注意网络和规范化战略
Qianxiao Yue1,2, Xiangrong Tong1,2
1School of Computer and Control Engineering, Yantai University, Yantai 264005, China.
Entropy (Basel, Switzerland)
|November 26, 2025
概括
联合推系统现在可以更好地学习用户偏好,避免过度安装. 我们的FedDMR模型使用双层注意网络和规范化来提高个性化建议的准确性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据 隐私 数据 隐私 数据
背景情况:
- 联合推 (FedRec) 系统旨在平衡个性化推与用户隐私.
- 由于局部用户交互数据有限,现有的FedRec模型面临挑战,阻碍了偏好学习,增加了过拟合风险.
研究的目的:
- 提出一个新的联邦推系统,FedDMR,解决当前FedRec模型的局限性.
- 加强互动信息的利用,并减轻分散推场景中的过度拟合.
主要方法:
- FedDMR采用双层多头注意网络来捕获和丰富来自本地交互的用户和项目嵌入.
- 规范化策略指导模型更新,限制偏离全球参数的偏差,以防止过拟合和提高通用性.
- 该系统使用联合学习框架进行分散的培训和参数聚合.
主要成果:
- 在三个数据集中,FedDMR在AUC和精度上平均有2.63%的改进.
- 拟议的模型显著超过了最近的联邦推基线.
结论:
- 通过丰富用户特征表示,FedDMR有效地在联合设置中增强了个性化偏好建模.
- 双层注意力网络和规范化策略成功地减轻了过度拟合,并提高了模型的通用性,从而带来了卓越的推性能.
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