Click-through rate (CTR) prediction is an important issue in online advertising and recommendation systems. It is used to estimate the likelihood that a user will click on ads. The method used in the traditional CTR prediction task is to improve the prediction effect through a large number of feature engineering. However, these methods are time-consuming and laborious, and the construction process is not universal. Due to the sparse and high-dimensional characteristics of data features, it is necessary to measure the importance of sparse features and obtain efficient feature interactions. In this paper, a novel CTR model based on Mutual Information and Feature Interaction (MiFiNN) is proposed. First, the mutual information of each sparse feature and the click result is calculated as the weight of each sparse feature. Subsequently, an interactive method combining the outer product and inner product is constructed to carry out the feature interaction. Then, the resulting feature interactions and the dense features set of the original input are taken as DNN inputs. We verify the proposed model on four datasets. In addition, several widely known models are introduced for comparison. The experimental results indicate the superiority of the model.

中文翻译：

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结合互信息特征加权和特征交互的点击率预测

点击率（CTR）预测是在线广告和推荐系统中的一个重要问题。它用于估计用户点击广告的可能性。传统CTR预测任务中采用的方法是通过大量的特征工程来提高预测效果。但这些方法费时费力，施工工艺不通用。由于数据特征的稀疏性和高维性，需要衡量稀疏特征的重要性，获得高效的特征交互。在本文中，提出了一种基于互信息和特征交互（MiFiNN）的新型点击率模型。首先计算每个稀疏特征和点击结果的互信息作为每个稀疏特征的权重。随后，构造外积和内积相结合的交互方法进行特征交互。然后，将生成的特征交互和原始输入的密集特征集作为 DNN 输入。我们在四个数据集上验证了所提出的模型。此外，还介绍了几个广为人知的模型进行比较。实验结果表明了该模型的优越性。