用专家分工+动态加权，提升跨域推荐精准度。
MoE-MLoRA for Multi-Domain CTR Prediction: Efficient Adaptation with Expert Specialization
- 每个专家专注一个领域，再由门控网络动态分配权重。
- 在淘宝数据集上提升1.45点加权AUC，表现显著更好。
- 适合复杂多变的跨域推荐场景，需根据模型调参。
个性化推荐系统需适应用户在不同领域的交互行为。传统方法如MLoRA在每个领域仅做一次适配，难以应对多样化用户行为。为此，我们提出MoE-MLoRA，一种混合专家框架：先独立训练各专家以实现领域专精，再通过门控网络动态加权其贡献。我们在Movielens和Taobao上的八个CTR模型上评估该方法，结果显示在大规模、动态数据集上（如Taobao-20）性能提升1.45点加权AUC；而在结构化程度高、领域多样性低且稀疏的数据集中收益有限。进一步分析表明，每个领域专家数量并非越多越好，需结合模型特性进行调优。研究揭示了基于专家架构在跨域推荐中的潜力，证明任务感知的专精与自适应加权可显著提升复杂环境下的预测精度。代码已开源。
原文摘要 · Abstract (English)
Personalized recommendation systems must adapt to user interactions across different domains. Traditional approaches like MLoRA apply a single adaptation per domain but lack flexibility in handling diverse user behaviors. To address this, we propose MoE-MLoRA, a mixture-of-experts framework where each expert is first trained independently to specialize in its domain before a gating network is trained to weight their contributions dynamically. We evaluate MoE-MLoRA across eight CTR models on Movielens and Taobao, showing that it improves performance in large-scale, dynamic datasets (+1.45 Weighed-AUC in Taobao-20) but offers limited benefits in structured datasets with low domain diversity and sparsity. Further analysis of the number of experts per domain reveals that larger ensembles do not always improve performance, indicating the need for model-aware tuning. Our findings highlight the potential of expert-based architectures for multi-domain recommendation systems, demonstrating that task-aware specialization and adaptive gating can enhance predictive accuracy in complex environments. The implementation and code are available in our GitHub repository.
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