With the explosive growth of Internet data, users are facing the problem of information overload, which makes it a challenge to efficiently obtain the required resources. Recommendation systems have emerged in this context. By filtering massive amounts of information, they provide users with content that meets their needs, playing a key role in scenarios such as advertising recommendation and product recommendation. However, traditional click-through rate prediction and TOP-K recommendation mechanisms are gradually unable to meet the recommendations needs in modern life scenarios due to high computational complexity, large memory consumption, long feature selection time, and insufficient feature interaction. This paper proposes a recommendations system model based on a separation embedding cross-network. The model uses an embedding neural network layer to transform sparse feature vectors into dense embedding vectors, and can independently perform feature cross operations on different dimensions, thereby improving the accuracy and depth of feature mining. Experimental results show that the model shows stronger adaptability and higher prediction accuracy in processing complex data sets, effectively solving the problems existing in existing models.

翻译：随着互联网数据的爆炸式增长，用户面临着信息过载的问题，高效获取所需资源成为一项挑战。推荐系统正是在此背景下应运而生。它通过过滤海量信息，为用户提供符合其需求的内容，在广告推荐、商品推荐等场景中发挥着关键作用。然而，传统的点击率预测与TOP-K推荐机制因计算复杂度高、内存消耗大、特征选择时间长以及特征交互不足等问题，逐渐难以满足现代生活场景中的推荐需求。本文提出一种基于分离嵌入交叉网络的推荐系统模型。该模型采用嵌入神经网络层将稀疏特征向量转化为稠密嵌入向量，并能够对不同维度独立进行特征交叉运算，从而提升特征挖掘的准确性与深度。实验结果表明，该模型在处理复杂数据集时表现出更强的适应性与更高的预测精度，有效解决了现有模型中存在的问题。