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.

翻译：在工业推荐系统中，存在多个为满足用户多样化兴趣和需求而设计的小程序。其样本空间仅为全空间的一个小子集，这使得训练高效模型具有挑战性。近年来，已有许多与跨域推荐相关的优秀研究，旨在缓解数据稀疏性问题。然而，其中少有工作同时考虑了样本与表征在持续迁移设定下对目标任务的适应性。为克服上述问题，我们提出了一种名为ECAT的全空间持续自适应迁移学习框架，该框架包含两个核心组件：首先，针对样本迁移，我们提出了一种实现由粗到细过程的两阶段方法。具体而言，我们通过图引导方法进行初步筛选，随后使用域适应方法进行细粒度筛选。其次，我们提出了一种自适应知识蒸馏方法，用于持续迁移在全空间数据集上训练良好的模型中的表征。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。在来自淘宝的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"带来了+13.6%的转化率和+8.6%的订单量提升。