Risk governance and optimization of the intelligent news algorithm recommendation mechanism

With the wide application of the intelligent news algorithm in the news industry, its recommendation mechanism has become the primary way for news consumers to obtain information. This study explores the intelligent news algorithm recommendation mechanism’s risk management measures and optimization schemes. Thus, people can get transparent news information more efficiently. Firstly, the study classifies and analyzes the risks of information filtering bias, information cocoon effect, and information bubble in intelligent news algorithm recommendation mechanism and collects and introduces large-scale news data as a data source. Secondly, the intelligent news algorithm recommendation model based on the convolutional neural network is constructed. The model uses word embedding technology to transform news articles into vector representations and trains the model to learn the feature representations of news articles and the correlation between them. Moreover, the loss function and weight of the model are adjusted to improve the diversity and balance of the recommendation results. Finally, simulation experiments are carried out to evaluate the model’s performance. The results reveal that the information diversity of the system model in this study is increased by 15%, and user satisfaction and the information quality index are increased by 10% and 7%. It proves the importance of diversified data sources, algorithm transparency and explainability, user feedback and participation, and balanced recommendation strategies to reduce risk and improve the performance of recommendation mechanisms. Therefore, the research results guide the practical application of the intelligent news algorithm recommendation mechanism and provide a reference for further improvement and optimization of the recommendation algorithm.