## 2 Related Work
In the subsequent improvement of the wide and deep models, the deep FM [21] model in 2017 focused on the wide part and improved the feature combination ability of the wide part using FM, whereas the NFM model [22] in the same year focused on improving the deep structure by adding a feature cross-pooling layer. The deep data-processing capabilities were further enhanced. In 2017, the AFM model [23], proposed by Alibaba, introduced an attention mechanism based on the NFM model, contributing to the multifield integration of recommendation systems. On the other hand, the development of computer vision also plays a good role in promoting the realization of multi-domain crossover technology. Convolutional networks are undoubtedly at the core of computer vision. As early as 1998, the emergence of LeNet [24] has marked an increase in CNNs. Although LeNet is a simple and small network from today's perspective, it is the first to completely define the structure of a CNN, which is crucial for its subsequent development. In 2012, [PERSON] and [PERSON] launched AlexNet [25], and in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC), he won 10.9% more than second place. Later, in 2014, [PERSON] and [PERSON] proposed a series of Visual Geometry Group Network (VGG) models [26], which appeared as the basic network that ranked second in the classification task and first in the localization task in the same year's ImageNet challenge. At that time, VGG was a very _deep_ network, reaching a depth of 19 layers. This was a significant breakthrough because theoretically, the fitting ability of the neural network model should increase with the _size_ of the model. In 2015, CNN ushered in qualitative changes. [PERSON] proposed ResNet [27], which not only solved the problem of neural network degradation, but also swept the competitors in ILSVRC and Common Objects in Context (COCO) competitions in the same year and won first place in classification, positioning, detection, and segmentation tasks. This is also a well-known residual network, the influence of which is phenomenal and provides a new idea for the design and optimization of almost all subsequent models. Recommendation systems have been developing and improving, and many new problems and research directions have been put forward in the latest research results in the last two years. Among them, causal inference is the recent hot spot, and the process of the recommendation results given by the recommendation system is regarded as causal inference, which introduces the concept of new intervention and puts forward the confounding factors that have not been observed. Around these concepts, recommendation systems for causal inference use two models, the exposure model and the result model, which ultimately provide the user with a list of recommendations. In the latest research [28; 29; 30], researchers respectively studied the enhancement of anchor knowledge graph generation of news recommendation reasoning, the three-dimensional recommendation system with self-supervision of social consciousness, and the elimination of popularity bias of recommendation system by agnostic counterfactual reasoning model. Causal inference is a very interesting entry point, but the problem at present is that the connection between the exposure model and result model is not very perfect, and the recommendation process using causal inference is very time-consuming, and there is therefore a lot of room for progress. We will try to learn and propose effective solutions in the following research. Another hotspot of recommendation systems is the development of Embedding technology. Embedding technology is the basis of all recommendation algorithms. One of its most important properties is its ability to transform feature data from high dimensional sparse vector to low dimensional dense vector, which is also the main role of Embedding in this paper. In addition, the Embedding vector itself is also a very important feature vector with stronger expression ability.