Click-through rate (CTR) prediction is one of the fundamental tasks for online advertising and recommendation. While multi-layer perceptron (MLP) serves as a core component in many deep CTR prediction models, it has been widely recognized that applying a vanilla MLP network alone is inefficient in learning multiplicative feature interactions. As such, many two-stream interaction models (e.g., DeepFM and DCN) have been proposed by integrating an MLP network with another dedicated network for enhanced CTR prediction. As the MLP stream learns feature interactions implicitly, existing research focuses mainly on enhancing explicit feature interactions in the complementary stream. In contrast, our empirical study shows that a well-tuned two-stream MLP model that simply combines two MLPs can even achieve surprisingly good performance, which has never been reported before by existing work. Based on this observation, we further propose feature gating and interaction aggregation layers that can be easily plugged to make an enhanced two-stream MLP model, FinalMLP. In this way, it not only enables differentiated feature inputs but also effectively fuses stream-level interactions across two streams. Our evaluation results on four open benchmark datasets as well as an online A/B test in our industrial system show that FinalMLP achieves better performance than many sophisticated two-stream CTR models. Our source code will be available at MindSpore/models.

翻译：点击率（CTR）预测是在线广告和推荐系统中的基础任务之一。尽管多层感知机（MLP）是许多深度CTR预测模型的核心组件，但已有研究表明，单独使用标准MLP网络在学习乘法特征交互方面效率较低。为此，研究人员提出了多种双流交互模型（如DeepFM和DCN），通过将MLP网络与另一个专用网络相结合，以增强CTR预测性能。现有研究主要聚焦于增强互补流中的显式特征交互，而MLP流则隐式地学习特征交互。相比之下，我们的实证研究表明，一个经过良好调优的、仅简单组合两个MLP的双流MLP模型也能取得令人惊讶的优异表现——这一发现此前尚未被任何现有工作报道。基于这一观察，我们进一步提出了可轻松嵌入的特征门控与交互聚合层，从而构建增强型双流MLP模型FinalMLP。该模型不仅能实现差异化的特征输入，还能有效融合两个流之间的流级交互。在四个公开基准数据集以及工业系统的在线A/B测试中，评估结果显示FinalMLP的性能优于多种复杂的双流CTR模型。我们的源代码将在MindSpore/models中开源。