  Modeling user's historical feedback is essential for Click-Through Rate
Prediction in personalized search and recommendation. Existing methods usually
only model users' positive feedback information such as click sequences which
neglects the context information of the feedback. In this paper, we propose a
new perspective for context-aware users' behavior modeling by including the
whole page-wisely exposed products and the corresponding feedback as
contextualized page-wise feedback sequence. The intra-page context information
and inter-page interest evolution can be captured to learn more specific user
preference. We design a novel neural ranking model RACP(i.e., Recurrent
Attention over Contextualized Page sequence), which utilizes page-context aware
attention to model the intra-page context. A recurrent attention process is
used to model the cross-page interest convergence evolution as denoising the
interest in the previous pages. Experiments on public and real-world industrial
datasets verify our model's effectiveness.
