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：作为全球最大的电商平台之一，淘宝的推荐系统服务于数亿用户的购物需求。点击率预测是推荐系统的核心组件。淘宝点击率预测的一大特点是存在多个推荐域，且不同域的规模差异显著。因此，进行跨域点击率预测以将知识从大域迁移至小域，从而缓解数据稀疏问题，至关重要。然而，现有跨域点击率预测方法仅适用于静态知识迁移，忽视了现实推荐系统中所有域均持续随时间演化的事实。鉴于此，我们提出一项必要且新颖的任务——持续迁移学习，旨在将知识从随时间演化的源域迁移至同样随时间演化的目标域。本文提出一种简单高效的持续迁移学习模型CTNet，用于解决淘宝持续跨域点击率预测问题，且CTNet可高效训练。特别地，CTNet考虑了工业界中模型已长期持续良好训练这一重要特性：CTNet旨在充分利用源域和目标域中已训练好的全部模型参数以避免历史知识丢失，且仅需增量目标域数据进行训练以保证效率。在淘宝进行的大规模离线实验和在线A/B测试验证了CTNet的效率和有效性。目前CTNet已部署于淘宝推荐系统线上环境，服务数亿活跃用户的主要流量。