Click-through rate (CTR) estimation is a fundamental task in personalized advertising and recommender systems and it's important for ranking models to effectively capture complex high-order features.Inspired by the success of ELMO and Bert in NLP field, which dynamically refine word embedding according to the context sentence information where the word appears, we think it's also important to dynamically refine each feature's embedding layer by layer according to the context information contained in input instance in CTR estimation tasks. We can effectively capture the useful feature interactions for each feature in this way. In this paper, We propose a novel CTR Framework named ContextNet that implicitly models high-order feature interactions by dynamically refining each feature's embedding according to the input context. Specifically, ContextNet consists of two key components: contextual embedding module and ContextNet block. Contextual embedding module aggregates contextual information for each feature from input instance and ContextNet block maintains each feature's embedding layer by layer and dynamically refines its representation by merging contextual high-order interaction information into feature embedding. To make the framework specific, we also propose two models(ContextNet-PFFN and ContextNet-SFFN) under this framework by introducing linear contextual embedding network and two non-linear mapping sub-network in ContextNet block. We conduct extensive experiments on four real-world datasets and the experiment results demonstrate that our proposed ContextNet-PFFN and ContextNet-SFFN model outperform state-of-the-art models such as DeepFM and xDeepFM significantly.

中文翻译：

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ContextNet：一种使用上下文信息来优化特征嵌入的点击率预测框架

点击率 (CTR) 估计是个性化广告和推荐系统中的一项基本任务，对于排序模型有效捕获复杂的高阶特征非常重要。 受到 ELMO 和 Bert 在 NLP 领域成功的启发，它们动态地改进了词嵌入根据单词出现的上下文句子信息，我们认为在CTR估计任务中根据输入实例中包含的上下文信息逐层动态细化每个特征的嵌入也是很重要的。我们可以通过这种方式有效地捕获每个特征的有用特征交互。在本文中，我们提出了一种名为 ContextNet 的新型 CTR 框架，该框架通过根据输入上下文动态优化每个特征的嵌入来隐式模拟高阶特征交互。具体来说，ContextNet 由两个关键组件组成：上下文嵌入模块和 ContextNet 块。上下文嵌入模块从输入实例中聚合每个特征的上下文信息，ContextNet 块逐层维护每个特征的嵌入，并通过将上下文高阶交互信息合并到特征嵌入中来动态改进其表示。为了使框架具体化，我们还通过在 ContextNet 块中引入线性上下文嵌入网络和两个非线性映射子网络，在该框架下提出了两个模型（ContextNet-PFFN 和 ContextNet-SFFN）。