As one of the largest e-commerce platforms in the world, Taobao's recommendation systems (RSs) serve the demands of shopping for hundreds of millions of customers. Click-Through Rate (CTR) prediction is a core component of the RS. One of the biggest characteristics in CTR prediction at Taobao is that there exist multiple recommendation domains where the scales of different domains vary significantly. Therefore, it is crucial to perform cross-domain CTR prediction to transfer knowledge from large domains to small domains to alleviate the data sparsity issue. However, existing cross-domain CTR prediction methods are proposed for static knowledge transfer, ignoring that all domains in real-world RSs are continually time-evolving. In light of this, we present a necessary but novel task named Continual Transfer Learning (CTL), which transfers knowledge from a time-evolving source domain to a time-evolving target domain. In this work, we propose a simple and effective CTL model called CTNet to solve the problem of continual cross-domain CTR prediction at Taobao, and CTNet can be trained efficiently. Particularly, CTNet considers an important characteristic in the industry that models has been continually well-trained for a very long time. So CTNet aims to fully utilize all the well-trained model parameters in both source domain and target domain to avoid losing historically acquired knowledge, and only needs incremental target domain data for training to guarantee efficiency. Extensive offline experiments and online A/B testing at Taobao demonstrate the efficiency and effectiveness of CTNet. CTNet is now deployed online in the recommender systems of Taobao, serving the main traffic of hundreds of millions of active users.

翻译：作为世界上最大的电子商务平台之一,道保的推荐系统(RSs)服务于为数亿客户购物的需求。 点击浏览率(CTR)预测是RS的核心组成部分。 道保的CTR预测的最大特点之一是,存在多个建议领域,不同领域的规模差异很大。 因此,必须进行跨域CTR预测,从大域向小域转移知识,以缓解数据散落问题。 然而,为静态知识转移提议了现有的跨域域域域域域CTR预测方法,忽略了现实世界RS的所有领域都在持续时间变化。 有鉴于此,我们提出了一个必要但新颖的任务,名为“连续传输”学习(CTL ), 将知识从时间变化源域向时间变化的目标领域转移。 在这项工作中,我们提议了一个简单有效的CTNet模型,叫做CTNet, 解决TaTOo的不断跨域域域域CTR预测问题,而CT网络服务现在可以有效地培训实时进行时间变化。 特别是, 不断将数据传输率测试作为数据库系统的重要特征,在不断使用。