CTR prediction has been widely used in the real world. Many methods model feature interaction to improve their performance. However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance. Recently, several methods tried to learn vector-level weights for feature representations to address the fixed representation issue. However, they only produce linear transformations to refine the fixed feature representations, which are still not flexible enough to capture the varying importance of each feature under different contexts. In this paper, we propose a novel module named Feature Refinement Network (FRNet), which learns context-aware feature representations at bit-level for each feature in different contexts. FRNet consists of two key components: 1) Information Extraction Unit (IEU), which captures contextual information and cross-feature relationships to guide context-aware feature refinement; and 2) Complementary Selection Gate (CSGate), which adaptively integrates the original and complementary feature representations learned in IEU with bit-level weights. Notably, FRNet is orthogonal to existing CTR methods and thus can be applied in many existing methods to boost their performance. Comprehensive experiments are conducted to verify the effectiveness, efficiency, and compatibility of FRNet.
翻译:在现实世界中广泛使用CTR预测。许多方法模式都以互动为特点,以改善其业绩。但是,大多数方法只学习每个特征的固定代表,而没有考虑到不同情况下每个特征的不同重要性,结果表现较差。最近,一些方法试图学习关于特征表现的矢量级权重,以解决固定代表问题。不过,它们只产生线性转变,以完善固定特征表现,这些变化仍然不够灵活,不足以反映不同情况下每个特征的不同重要性。在本文中,我们提议了一个名为“地物精度精度网络”的新模块,该模块学习不同情况下每个特征在位值上的环境觉悟特征表现。 FRNet由两个关键组成部分组成:(1) 信息提取股(IEU),该单位收集背景信息和跨环境特征表现关系,以指导对地貌特征的改进;和(2) 补充选择门(SGate),该平台将IEU所学的原始和互补特征表现与位数重量相容。值得注意的是,FRNet与现有的CTR方法或多位化,因此可以应用于许多现有方法,以提高其效能。全面试验,以核查和FRFR的兼容性。