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.
翻译:点击率( CTR) 估测是个人化广告和建议系统的一项基本任务,对于排名模型来说,重要的是要有效捕捉复杂的高顺序特征。 受 NLP 字段ELMO 和 Bert 成功成功激励, 并受 NLP 字段ELMO 和 Bert 的启发, 后者根据字词出现时的上下文句信息动态地精细嵌字, 我们认为根据CTR 估测任务中输入实例所载信息, 动态地细化每个特性的层嵌入层也很重要。 我们可以有效地捕捉每个特性的有用特征互动。 在本文中, 我们提议一个名为CTR 框架, 隐含地模拟高顺序, 通过动态地精细化每个特性的深度互动。