Click-through rate (CTR) prediction is a critical task in online advertising and recommendation systems, as accurate predictions are essential for user targeting and personalized recommendations. Most recent cutting-edge methods primarily focus on investigating complex implicit and explicit feature interactions. However, these methods neglect the issue of false correlations caused by confounding factors or selection bias. This problem is further magnified by the complexity and redundancy of these interactions. We propose a CTR prediction framework that removes false correlation in multi-level feature interaction, termed REFORM. The proposed REFORM framework exploits a wide range of multi-level high-order feature representations via a two-stream stacked recurrent structure while eliminating false correlations. The framework has two key components: I. The multi-level stacked recurrent (MSR) structure enables the model to efficiently capture diverse nonlinear interactions from feature spaces of different levels, and the richer representations lead to enhanced CTR prediction accuracy. II. The false correlation elimination (FCE) module further leverages Laplacian kernel mapping and sample reweighting methods to eliminate false correlations concealed within the multi-level features, allowing the model to focus on the true causal effects. Extensive experiments based on four challenging CTR datasets and our production dataset demonstrate that the proposed REFORM model achieves state-of-the-art performance. Codes, models and our dataset will be released at https://github.com/yansuoyuli/REFORM.

翻译：点击率预测是在线广告与推荐系统中的关键任务，准确的预测对用户定向和个性化推荐至关重要。当前多数前沿方法主要聚焦于研究复杂的隐式和显式特征交互。然而，这些方法忽略了由混淆因素或选择偏差导致的虚假相关性问题，而交互的复杂性与冗余性进一步放大了该问题。我们提出了一种消除多层级特征交互中虚假相关性的点击率预测框架，称为REFORM。该框架通过双流堆叠递归结构，在消除虚假相关性的同时获取广泛的多层级高阶特征表示。框架包含两个核心组件：一、多层级堆叠递归结构使模型能够从不同层级的特征空间中有效捕获多样非线性交互，更丰富的表示提升了点击率预测精度；二、虚假相关性消除模块进一步利用拉普拉斯核映射与样本重加权方法，消除隐藏在多层级特征中的虚假相关性，使模型聚焦于真实因果效应。基于四个挑战性CTR数据集及生产数据集的广泛实验表明，所提出的REFORM模型达到了当前最优性能。相关代码、模型及数据集将在https://github.com/yansuoyuli/REFORM 发布。