提出分离嵌入与自注意力结合的新模型，提升推荐系统特征挖掘能力。
A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining
- 采用分离嵌入跨网络，分维度独立进行特征交叉
- 在复杂数据集上准确率更高，适应性更强
- 适合需要深度特征交互的推荐场景
随着互联网数据爆炸式增长，用户面临信息过载问题，高效获取所需资源成为挑战。推荐系统通过过滤海量信息，为用户提供符合需求的内容，在广告推荐和产品推荐等场景中发挥关键作用。然而，传统点击率预测与TOP-K推荐机制因计算复杂度高、内存消耗大、特征选择耗时长及特征交互不足，难以满足现代应用场景需求。本文提出一种基于分离嵌入跨网络的推荐模型，利用嵌入神经网络层将稀疏特征向量转换为稠密嵌入向量，并可对不同维度独立执行特征交叉操作，从而提升特征挖掘的精度与深度。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准确性，有效解决了现有模型存在的问题。
原文摘要 · Abstract (English)
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.
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