Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search
This work addresses personalized search and recommendation in e-commerce, offering an incremental improvement by incorporating context information often neglected in existing methods.
The paper tackles the problem of click-through rate prediction in e-commerce search by modeling users' contextualized page-wise feedback, including both exposed products and feedback, to capture intra-page context and inter-page interest evolution, resulting in improved performance verified on public and industrial datasets.
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.