Jianjun Wei

Bingyao Liu, Jiajing Chen, Rui Wang et al.

The development of Internet technology has led to a rapid increase in news information. Filtering out valuable content from complex information has become an urgentproblem that needs to be solved. In view of the shortcomings of traditional manual classification methods that are time-consuming and inefficient, this paper proposes an automaticclassification scheme for news texts based on deep learning. This solution achieves efficient classification and management of news texts by introducing advanced machine learning algorithms, especially an optimization model that combines Bi-directional Long Short-Term Memory Network (Bi-LSTM) and Attention Mechanism. Experimental results show that this solution can not only significantly improve the accuracy and timeliness of classification, but also significantly reduce the need for manual intervention. It has important practical significance for improving the information processing capabilities of the news industry and accelerating the speed of information flow. Through comparative analysis of multiple common models, the effectiveness and advancement of the proposed method are proved, laying a solid foundation for future news text classification research.

Wenyi Liu, Rui Wang, Yuanshuai Luo et al.

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.