Click-Through Rate (CTR) prediction, is an essential component of online advertising. The mainstream techniques mostly focus on feature interaction or user interest modeling, which rely on users' directly interacted items. The performance of these methods are usally impeded by inactive behaviours and system's exposure, incurring that the features extracted do not contain enough information to represent all potential interests. For this sake, we propose Neighbor-Interaction based CTR prediction, which put this task into a Heterogeneous Information Network (HIN) setting, then involves local neighborhood of the target user-item pair in the HIN to predict their linkage. In order to enhance the representation of the local neighbourhood, we consider four types of topological interaction among the nodes, and propose a novel Graph-masked Transformer architecture to effectively incorporates both feature and topological information. We conduct comprehensive experiments on two real world datasets and the experimental results show that our proposed method outperforms state-of-the-art CTR models significantly.
翻译:点击浏览率( CTR) 预测是在线广告的一个基本组成部分。 主流技术主要侧重于特征互动或用户兴趣模型, 依赖用户直接互动的项目。 这些方法的性能受到非活动行为和系统暴露的阻碍, 由此得出的特征没有包含足够信息来代表所有潜在利益。 为此, 我们提议基于邻居互动的 CTR 预测, 将这项任务引入异质信息网络( HIN) 设置, 然后让目标用户项目对的本地邻居参与到 HIN 中来预测它们的联系。 为了提高本地邻居的代表性, 我们考虑在节点之间四种类型的顶层互动, 并提出一个新的图形化变异器结构, 以有效整合特征和表面信息。 我们对两个真实的世界数据集进行全面实验, 实验结果显示, 我们提出的方法大大超越了最新的 CTR 模型 。