  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.
