ECAT：面向跨域推荐的全空间持续自适应迁移学习框架
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation
AI总结：
针对工业推荐中小程序样本稀疏且现有跨域方法未同时考虑样本与表征持续迁移适应性的问题，本文提出 ECAT 框架，通过两阶段样本筛选和自适应知识蒸馏实现全空间持续自适应迁移，在淘宝数据上显著提升 CVR 和订单量。
摘要：
在工业推荐系统中，存在多个旨在满足用户多样化兴趣与需求的小程序（mini-app）。它们的样本空间仅为全空间的一小部分，这使得训练高效模型变得困难。近年来，已有许多与跨域推荐相关的优秀研究，旨在缓解数据稀疏问题。然而，很少有研究同时考虑样本迁移和表征持续迁移设置对目标任务的适应性。为克服上述问题，我们提出了一个全空间持续自适应迁移学习框架，称为 ECAT，其包含两个核心组件：第一，在样本迁移方面，我们提出了一种两阶段方法，实现由粗到细的过程。具体而言，我们首先通过图引导方法进行初步选择，随后利用领域自适应方法进行细粒度选择。第二，我们提出了一种自适应知识蒸馏方法，用于持续迁移一个在全空间数据集上训练良好的模型所学习到的表征。ECAT 能够在目标任务的监督下充分利用全空间样本和表征，同时避免负迁移。在淘宝真实工业数据集上的综合实验表明，ECAT 在离线指标上取得了最先进的性能，并为淘宝知名小程序“百亿补贴”（Baiyibutie）带来了 +13.6% 的 CVR 提升和 +8.6% 的订单增长。
英文摘要：
In industrial recommendation systems, there are several mini-apps designed to meet the diverse interests and needs of users. The sample space of them is merely a small subset of the entire space, making it challenging to train an efficient model. In recent years, there have been many excellent studies related to cross-domain recommendation aimed at mitigating the problem of data sparsity. However, few of them have simultaneously considered the adaptability of both sample and representation continual transfer setting to the target task. To overcome the above issue, we propose a Entire space Continual and Adaptive Transfer learning framework called ECAT which includes two core components: First, as for sample transfer, we propose a two-stage method that realizes a coarse-to-fine process. Specifically, we perform an initial selection through a graph-guided method, followed by a fine-grained selection using domain adaptation method. Second, we propose an adaptive knowledge distillation method for continually transferring the representations from a model that is well-trained on the entire space dataset. ECAT enables full utilization of the entire space samples and representations under the supervision of the target task, while avoiding negative migration. Comprehensive experiments on real-world industrial datasets from Taobao show that ECAT advances state-of-the-art performance on offline metrics, and brings +13.6% CVR and +8.6% orders for Baiyibutie, a famous mini-app of Taobao.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
