A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Mining
This addresses computational inefficiencies in recommendation systems for users facing information overload, though it appears to be an incremental improvement over existing embedding methods.
The paper tackles the problem of inefficient feature mining in recommendation systems by proposing a separation embedding cross-network model that transforms sparse features into dense embeddings and performs cross-dimensional feature operations. Experimental results show the model achieves higher prediction accuracy and better adaptability on complex datasets.
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.