An AI-enabled approach for improving advertising identification and promotion in social networks

The rapid development of the social economy has considerably impacted the traditional advertising industry. In terms of identifying and recommending advertisements, improving the accuracy of advertisement promotion and identification is an urgent need. Artificial intelligence (AI) methods are of practical application to facilitate digital transformation in the advertising industry. Among the many AI methods, the network model based on the genetic algorithm back propagation (GABP) neural network is the most compatible with applications in the advertising industry. In this work, the GABP neural network is applied in the construction of social networks to predict the click-through rate (CTR) of website advertising applications through the optimization of advertising promotion strategies. In the processes of optimizing and improving the system model using AI methods, the technological focus is the accuracy of advertising promotion and identification for the developing advertising industry. First, background information on the era of internet and AI development is analyzed. The new media technology is discussed through AI research and traditional advertising industry literature. In addition, a CTR prediction model is created for advertising applications based on the GABP network. In the neural network improvement process, the performance of GABP is enhanced through multiple iterations by optimizing the application strategy of advertising scenarios according to the topology of network connections. The study results demonstrate that different algorithms' recognition accuracy and precision show an increasing trend as the number of model iterations increases. The recognition accuracy of GABP increases from 49 % to 72 %, and the recognition precision of the algorithm increases from 69 % to 86 %. In addition, the area under the curve (AUC) value of the GABP network is only 0.6 before the number of neurons increases. When the number of neurons is 400, the AUC value of the algorithm reaches 0.82, and the comprehensive diagnostic value of the system dramatically improves. This research has significant reference value for reforming the traditional advertising work mode, enhancing intellectual development and promoting resource efficiency in the advertising industry.