Based on intelligent advertising recommendation and abnormal advertising monitoring system in the field of machine learning

Bo Liu

doi:10.62051/ijcsit.v1n1.03

Authors

Bo Liu

DOI:

https://doi.org/10.62051/ijcsit.v1n1.03

Keywords:

Intelligent advertising recommendation, Click rate prediction, Abnormal advertising monitoring, Machine learning

Abstract

With the rapid development of the Internet, the scale of the online advertising market has expanded rapidly, and display advertising has become the most popular means of publicity. Accurate advertising recommendation is the guarantee of Internet platform revenue, and accurate advertising click rate prediction is the premise of accurate recommendation and abnormal advertising detection. Therefore, such monitoring and recommendation can be achieved through machine learning combined with artificial intelligence, and the application of intelligent AD recommendation systems and abnormal AD monitoring in the field of machine learning represents a complex integration of technologies to improve the precision and effectiveness of digital marketing strategies. Intelligent AD recommendation systems utilize advanced machine learning algorithms to analyze user behavior and preferences to deliver tailored AD content. These systems leverage vast amounts of user data, including browsing history, purchase history, and engagement metrics, to predict and present the most relevant ads. This paper analyzes the data mining in machine learning algorithms and the real-time online recommendation algorithm of Gaussian process, and analyzes the abnormal advertising monitoring system for maintaining the integrity and efficiency of advertising campaigns. By using machine learning technology for pattern recognition and anomaly detection, various measures and indicators of advertising campaigns can be monitored in a vigilant manner.

Downloads

Download data is not yet available.

References

Grace M C , Zhou W , Jiang X , et al. Unsafe Exposure Analysis of Mobile In-App Advertisements ABSTRACT[C// Acm Conference on Security & Privacy inWireless & Mobile Networks.ACM2012.

Tianbo, Song, Hu Weijun, Cai Jiangfeng, Liu Weijia, Yuan Quan, and He Kun. "Bio-inspired Swarm Intelligence: a Flocking Project With Group Object Recognition." In 2023 3rd International Conference on Consumer Electronics and Computer Engineering (ICCECE), pp. 834-837. IEEE, 2023.DOI: 10.1109/mce.2022.3206678

Che, C., Liu, B., Li, S., Huang, J., & Hu, H. (2023). Deep learning for precise robot position prediction in logistics. Journal of Theory and Practice of Engineering Science, 3(10), 36-41.

Liu, B., Zhao, X., Hu, H., Lin, Q., & Huang, J. (2023). Detection of Esophageal Cancer Lesions Based on CBAM Faster R-CNN. Journal of Theory and Practice of Engineering Science, 3(12), 36–42.

Liu, Bo, et al. "Integration and Performance Analysis of Artificial Intelligence and Computer Vision Based on Deep Learning Algorithms." arXiv preprint arXiv:2312.12872 (2023).

Li, Linxiao, et al. "Zero-resource knowledge-grounded dialogue generation." Advances in Neural Information Processing Systems 33 (2020): 8475-8485.

Shen, Zepeng, et al. "The Application of Artificial Intelligence to The Bayesian Model Algorithm for Combining Genome Data." Academic Journal of Science and Technology 8.3 (2023): 132-135.

Zong, Yanqi, et al. "Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset." Journal of Theory and Practice of Engineering Science 3.12 (2023): 29-35.

Zhang, Quan, et al. "Deep Learning Model Aids Breast Cancer Detection." Frontiers in Computing and Intelligent Systems 6.1 (2023): 99-102.

Xu, Jingyu, et al. "Based on TPUGRAPHS Predicting Model Runtimes Using Graph Neural Networks." Frontiers in Computing and Intelligent Systems 6.1 (2023): 66-69.

Liu, Yuxiang, et al. "Grasp and Inspection of Mechanical Parts based on Visual Image Recognition Technology." Journal of Theory and Practice of Engineering Science 3.12 (2023): 22-28.

Zong, Yanqi, et al. “Improvements and Challenges in StarCraft II Macro-Management A Study on the MSC Dataset”. Journal of Theory and Practice of Engineering Science, vol. 3, no. 12, Dec. 2023, pp. 29-35, doi:10.53469/jtpes.2023.03(12).05.