# A New Click-Through Rates Prediction Model Based on Deep&Cross Network
## 1 Introduction
Then, DeepFM model came from Reference [17], which can learn both low-order features and high-order features at the same time and can enable faster training and achieve more accurate prediction. Then, Google proposed Deep&Cross Network (DCN) model, in Reference [18], for click rate prediction and personalized recommendations. The model is equipped with a cross network, which can effectively learn the feature interactions and greatly improve the effect of click rate prediction and personalized recommendations. Alibaba introduced Deep Interest Network (DIN), in Reference [19]. And this model captures the diversified interests of users as interest distribution, adopts attention mechanism to improve the weight of relevant and effective interests, and reduces the impact of unrelated interests. This model also adds a local perceptual feature similar to Convolutional Neural Networks (CNN), which can well perceive users' interests in the target goods. The final click rate of the target goods is estimated according to the interest distribution of users. In [20], [PERSON] puts forward the Feature Importance and Bilinear feature Interaction Network (FiBiNET) model that presents senet mechanism, which can realize dynamic learning of feature weights, as well as introduces a new bilinear function for feature interaction learning. And, more recently, Alibaba further proposed the Deep Session Interest Network (DSIN) model, in Reference [21]. DSIN models users' behavior sequences on their historical sessions, resulting in more precise and efficient prediction. Additionally, CTR based on Deep Neural Networks (DNN) can be applied in other domains, like information retrieval, such as Reference [22], where a novel end-to-end cross-media semantic correlation learning model based on a deep hash network and semantic expansion for social network cross-media search (DHNS) are presented. The introduction of DNNs in CTR prediction has gained significant improvements in performance. Compared with traditional machine learning algorithms, such as logistic regression and factorization machine, DNN-based CTR model can better deal with the combination of feature vectors and higher-order features, and it has structural advantages in improving prediction accuracy. However, there are still a lot of challenges to be addressed, which are as follows:* The modeling of feature interactions is still limited. When a neural network learns high-order features, it is unable to effectively identify the importance and association of combined features, which further restricts the prediction accuracy of the model. * Noise interference can be introduced in the embedding phase, and then it can skew the final prediction results. * The data for CTR prediction is unbalanced, which leads to the issue that the classic deep model can not predict accurately, since it needs a lot of data of the same category to learn the discriminative features. * The optimization algorithm to train the DNN may not work well because of the underlying distribution of data may be more complex than the assumption. And this paper is to address these issues. In summary, the main contributions of this paper are as follows:  
* A new end-to-end model of FO-FTRL-DCN has been introduced, based on Deep&Cross Network (DCN) model [18], augmented with FTRL [11]. * Some preprocessing techniques, such as Synthetic Minority Oversampling Technique (SMOTE) [23], have been applied to optimize FO-FTRL-DCN, which aim to denoise and balance the data, leading to better performance. * The proposed FO-FTRL-DCN model has been evaluated on multiple datasets of the prestigious benchmark of iPinYou, showing that the model has better performance compared with state-of-the-art baselines, and better generalization across different datasets. The paper is structured as follows. Section 2 presents some preliminaries, and Section 3 elaborates the proposed model FO-FTRL-DCN. And Section 4 is for experimental results and discussion, with Section 5 concluding the paper.