Recently, click-through rate (CTR) prediction is a challenge problem in the aspect of online advertising. Some researchers have proposed deep learning-based models that follow a similar embedding and MLP paradigm. However, the corresponding approaches generally ignore the importance of capturing the latent user interest behind user behaviour data. In this paper, we present a novel attentive deep interest-based network model called ADIN. Specifically, we capture the interest sequence in the interest extractor layer, and the auxiliary losses are employed to produce the interest state with the deep supervision. First, we model the dependency between behaviours by using a bidirectional gated recurrent unit (Bi-GRU). Next, we extract the interest evolving process that is related to the target and propose an interest evolving layer. At the same time, attention mechanism is embedded into the sequential structure. Then, the model learns highly non-linear interactions of features based on stack autoencoders. An experiment has been done using four real-world datasets, the proposed model achieves superior performance than the existing state-of-the-art models.

中文翻译：

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注意力机制基于用户兴趣的点击率预测模型

最近，点击率（CTR）预测是在线广告方面的挑战性问题。一些研究人员提出了基于深度学习的模型，该模型遵循类似的嵌入和MLP范例。但是，相应的方法通常忽略捕获用户行为数据背后的潜在用户兴趣的重要性。在本文中，我们提出了一种新颖的，基于深层兴趣的，基于兴趣的网络模型，称为ADIN。具体来说，我们在兴趣提取器层中捕获兴趣序列，并使用辅助损耗在深度监督下生成兴趣状态。首先，我们通过使用双向门控循环单元（Bi-GRU）对行为之间的依赖性进行建模。接下来，我们提取与目标相关的兴趣演化过程，并提出兴趣演化层。与此同时，注意机制嵌入到顺序结构中。然后，该模型基于堆栈自动编码器学习高度非线性的要素交互。已使用四个真实世界的数据集进行了实验，所提出的模型比现有的最新模型具有更高的性能。