Deep neural networks (DNNs) that incorporated lifelong sequential modeling (LSM) have brought great success to recommendation systems in various social media platforms. While continuous improvements have been made in domain-specific LSM, limited work has been done in cross-domain LSM, which considers modeling of lifelong sequences of both target domain and source domain. In this paper, we propose Lifelong Cross Network (LCN) to incorporate cross-domain LSM to improve the click-through rate (CTR) prediction in the target domain. The proposed LCN contains a LifeLong Attention Pyramid (LAP) module that comprises of three levels of cascaded attentions to effectively extract interest representations with respect to the candidate item from lifelong sequences. We also propose Cross Representation Production (CRP) module to enforce additional supervision on the learning and alignment of cross-domain representations so that they can be better reused on learning of the CTR prediction in the target domain. We conducted extensive experiments on WeChat Channels industrial dataset as well as on benchmark dataset. Results have revealed that the proposed LCN outperforms existing work in terms of both prediction accuracy and online performance.

翻译：深度神经网络（DNNs）结合终身序列建模（LSM）已在各类社交媒体平台的推荐系统中取得显著成功。尽管领域特定LSM持续取得改进，但跨域LSM的相关研究仍较为有限，后者需同时考虑目标域和源域的终身序列建模。本文提出终身跨域网络（LCN）以引入跨域LSM，从而提升目标域的点击率（CTR）预测性能。所提LCN包含一个终身注意力金字塔（LAP）模块，该模块由三级级联注意力构成，可有效提取候选物品在终身序列中的兴趣表示。我们进一步提出跨表示生成（CRP）模块，通过对跨域表示的学习与对齐施加额外监督，使其能更好地复用于目标域CTR预测任务。我们在微信视频号工业数据集及基准数据集上开展了大量实验，结果表明，所提LCN在预测精度与在线性能方面均优于现有方法。