In the Click-Through Rate (CTR) prediction scenario, user's sequential
behaviors are well utilized to capture the user interest in the recent
literature. However, despite being extensively studied, these sequential
methods still suffer from three limitations. First, existing methods mostly
utilize attention on the behavior of users, which is not always suitable for
CTR prediction, because users often click on new products that are irrelevant
to any historical behaviors. Second, in the real scenario, there exist numerous
users that have operations a long time ago, but turn relatively inactive in
recent times. Thus, it is hard to precisely capture user's current preferences
through early behaviors. Third, multiple representations of user's historical
behaviors in different feature subspaces are largely ignored. To remedy these
issues, we propose a Multi-Interactive Attention Network (MIAN) to
comprehensively extract the latent relationship among all kinds of fine-grained
features (e.g., gender, age and occupation in user-profile). Specifically, MIAN
contains a Multi-Interactive Layer (MIL) that integrates three local
interaction modules to capture multiple representations of user preference
through sequential behaviors and simultaneously utilize the fine-grained
user-specific as well as context information. In addition, we design a Global
Interaction Module (GIM) to learn the high-order interactions and balance the
different impacts of multiple features. Finally, Offline experiment results
from three datasets, together with an Online A/B test in a large-scale
recommendation system, demonstrate the effectiveness of our proposed approach.

通过提出一种 Multi-Interactive Attention Network (MIAN) 方法，结合多种精细特征以及性别、年龄、职业等用户个人信息，从多个方面综合提取用户偏好，同时设计全局交互模块 (GIM) 学习高阶交互和平衡多个特征的影响，能够有效地预测用户点击率 (CTR)。