Application of Large Language Models in Personalized Advertising Recommendation Systems
DOI:
https://doi.org/10.5281/zenodo.13144319ARK:
https://n2t.net/ark:/40704/JIEAS.v2n4a19Keywords:
Large Language Models, Personalized Advertising, Recommendation Systems, User Behavior Analysis, Content GenerationAbstract
This paper explores the application of Large Language Models (LLMs) in personalized advertising recommendation systems. It delves into the methodologies of using LLMs to analyze user behavior, generate personalized content, and enhance recommendation accuracy. The study employs a comprehensive data collection and preprocessing framework to ensure the robustness and reliability of the findings. Through a comparative study with traditional recommendation systems, the paper demonstrates the potential advantages of LLMs in improving user engagement and satisfaction. Key performance metrics such as precision, recall, and F1-score are used to evaluate the effectiveness of the LLM-based system. Furthermore, the paper examines the computational challenges and data privacy concerns associated with LLM integration. It also discusses the potential for future advancements in LLM technology to further optimize personalized advertising strategies. The paper concludes with a set of proposed solutions and directions for future research, highlighting the transformative impact of LLMs on personalized advertising.
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Zhang, B., Xiao, J., Yan, H., Yang, L., & Qu, P. (2024). Review of NLP Applications in the Field of Text Sentiment Analysis. Journal of Industrial Engineering and Applied Science, 2(3), 28-34.
Zhou, J., Liang, Z., Fang, Y., & Zhou, Z. (2024). Exploring Public Response to ChatGPT with Sentiment Analysis and Knowledge Mapping. IEEE Access.
Su, J., Jiang, C., Jin, X., Qiao, Y., Xiao, T., Ma, H., ... & Lin, J. (2024). Large language models for forecasting and anomaly detection: A systematic literature review. arXiv preprint arXiv:2402.10350.
Yan, H., Xiao, J., Zhang, B., Yang, L., & Qu, P. (2024). The Application of Natural Language Processing Technology in the Era of Big Data. Journal of Industrial Engineering and Applied Science, 2(3), 20-27.
Zhang, B., Yan, H., Wu, J., & Qu, P. (2024). Application of Semantic Analysis Technology in Natural Language Processing. Journal of Computer Technology and Applied Mathematics, 1(2), 27-34.
Qu, P., Zhang, B., Wu, J., & Yan, H. (2024). Comparison of Text Classification Algorithms based on Deep Learning. Journal of Computer Technology and Applied Mathematics, 1(2), 35-42.
Wang, L., Fang, W., & Du, Y. (2024). Load Balancing Strategies in Heterogeneous Environments. Journal of Computer Technology and Applied Mathematics, 1(2), 10-18.
Zou, Z., Careem, M., Dutta, A., & Thawdar, N. (2023). Joint spatio-temporal precoding for practical non-stationary wireless channels. IEEE Transactions on Communications, 71(4), 2396-2409.
Zou, Z., Careem, M., Dutta, A., & Thawdar, N. (2022, May). Unified characterization and precoding for non-stationary channels. In ICC 2022-IEEE International Conference on Communications (pp. 5140-5146). IEEE.
Zou, Z.-B., Song, L.-P., & Song, Z.-L. (2017). Labeled box-particle PHD filter for multi-target tracking. 2017 3rd IEEE International Conference on Computer and Communications (ICCC), 1725–1730. IEEE.
Liu, T., Xu, C., Qiao, Y., Jiang, C., & Chen, W. (2024). News Recommendation with Attention Mechanism. Journal of Industrial Engineering and Applied Science, 2(1), 21-26.
Liu, T., Cai, Q., Xu, C., Hong, B., Ni, F., Qiao, Y., & Yang, T. (2024). Rumor Detection with A Novel Graph Neural Network Approach. Academic Journal of Science and Technology, 10(1), 305-310.
Liu, T., Cai, Q., Xu, C., Hong, B., Xiong, J., Qiao, Y., & Yang, T. (2024). Image Captioning in News Report Scenario. Academic Journal of Science and Technology, 10(1), 284-289.
Xu, C., Yu, J., Chen, W., & Xiong, J. (2024, January). Deep learning in photovoltaic power generation forecasting: Cnn-lstm hybrid neural network exploration and research. In The 3rd International Scientific and Practical Conference (Vol. 363, p. 295).
Peng, Q., Zheng, C., & Chen, C. (2024). A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2240-2249).
Yi, X., & Qiao, Y. (2024). GPU-Based Parallel Computing Methods for Medical Photoacoustic Image Reconstruction. arXiv preprint arXiv:2404.10928.
Zhou, Z. (2024, February). ADVANCES IN ARTIFICIAL INTELLIGENCE-DRIVEN COMPUTER VISION: COMPARISON AND ANALYSIS OF SEVERAL VISUALIZATION TOOLS. In The 8th International scientific and practical conference “Priority areas of research in the scientific activity of teachers”(February 27–March 01, 2024) Zagreb, Croatia. International Science Group. 2024. 298 p. (p. 224).
Wu, R. (2024). Leveraging Deep Learning Techniques in High-Frequency Trading: Computational Opportunities and Mathematical Challenges. Academic Journal of Sociology and Management, 2(4), 27-34.
Zou, Z., Careem, M., Dutta, A., & Thawdar, N. (2022). Unified characterization and precoding for non-stationary channels. ICC 2022-IEEE International Conference on Communications, 5140–5146. IEEE.
Zhibin, Z. O. U., Liping, S., & Xuan, C. (2019). Labeled box-particle CPHD filter for multiple extended targets tracking. Journal of Systems Engineering and Electronics, 30(1), 57–67.
Liu, T., Xu, C., Qiao, Y., Jiang, C., & Yu, J. (2024). Particle Filter SLAM for Vehicle Localization. Journal of Industrial Engineering and Applied Science, 2(1), 27-31.
Feng, M., Wang, X., Zhao, Z., Jiang, C., Xiong, J., & Zhang, N. (2024). Enhanced Heart Attack Prediction Using eXtreme Gradient Boosting. Journal of Theory and Practice of Engineering Science, 4(04), 9-16.
Xiong, J., Jiang, C., Zhao, Z., Qiao, Y., Zhang, N., Feng, M., & Wang, X. (2024). Selecting the Best Fit Software Programming Languages: Using BERT for File Format Detection. Journal of Theory and Practice of Engineering Science, 4(06), 20-28.
Peng, Q., Zheng, C., & Chen, C. Source-free Domain Adaptive Human Pose Estimation (Supplementary Material).
Li, W. (2024). The Impact of Apple's Digital Design on Its Success: An Analysis of Interaction and Interface Design. Academic Journal of Sociology and Management, 2(4), 14-19.
Xu, Y., Lin, Y. S., Zhou, X., & Shan, X. (2024). Utilizing emotion recognition technology to enhance user experience in real-time. Computing and Artificial Intelligence, 2(1), 1388-1388.
Zhou, Z., Xu, C., Qiao, Y., Xiong, J., & Yu, J. (2024). Enhancing Equipment Health Prediction with Enhanced SMOTE-KNN. Journal of Industrial Engineering and Applied Science, 2(2), 13-20.
Liu, S., Wu, K., Jiang, C., Huang, B., & Ma, D. (2023). Financial Time-Series Forecasting: Towards Synergizing Performance And Interpretability Within a Hybrid Machine Learning Approach. arXiv e-prints, arXiv-2401.
Peng, Q., Zheng, C., & Chen, C. (2023). Source-free domain adaptive human pose estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4826-4836).
Wang, L. (2024). The Impact of Network Load Balancing on Organizational Efficiency and Managerial Decision-Making in Digital Enterprises. Academic Journal of Sociology and Management, 2(4), 41-48.
Jia, J., Wang, N., Liu, Y., & Li, H. (2024). Fast Two-Grid Finite Element Algorithm for a Fractional Klein-Gordon Equation. Contemporary Mathematics, 1164-1180.
Xiong, J., Feng, M., Wang, X., Jiang, C., Zhang, N., & Zhao, Z. (2024). Decoding sentiments: Enhancing covid-19 tweet analysis through bert-rcnn fusion. Journal of Theory and Practice of Engineering Science, 4(01), 86-93.
Zhao, Z., Zhang, N., Xiong, J., Feng, M., Jiang, C., & Wang, X. (2024). Enhancing E-commerce Recommendations: Unveiling Insights from Customer Reviews with BERTFusionDNN. Journal of Theory and Practice of Engineering Science, 4(02), 38-44.
Zhang, N., Xiong, J., Zhao, Z., Feng, M., Wang, X., Qiao, Y., & Jiang, C. (2024). Dose My Opinion Count? A CNN-LSTM Approach for Sentiment Analysis of Indian General Elections. Journal of Theory and Practice of Engineering Science, 4(05), 40-50.
Wang, L. (2024). Network Load Balancing Strategies and Their Implications for Business Continuity. Academic Journal of Sociology and Management, 2(4), 8-13.
Li, K., Zhu, A., Zhou, W., Zhao, P., Song, J., & Liu, J. (2024). Utilizing deep learning to optimize software development processes. arXiv preprint arXiv:2404.13630.
Song, C. (2024). Optimizing Management Strategies for Enhanced Performance and Energy Efficiency in Modern Computing Systems. Academic Journal of Sociology and Management, 2(4), 57-64.
Song, C. (2024). Optimizing Management Strategies for Enhanced Performance and Energy Efficiency in Modern Computing Systems. Academic Journal of Sociology and Management, 2(4), 57-64.
Zhu, M., Zhang, Y., Gong, Y., Xu, C., & Xiang, Y. Enhancing Credit Card Fraud Detection: A Neural Network and SMOTE Integrated Approach. Journal of Theory and Practice of Engineering Science ISSN, 2790, 1513.
Li, K., Zhao, P., Dai, S., Zhu, A., Hong, B., Liu, J., ... & Zhang, Y. (2024). Exploring the Impact of Quantum Computing on Machine Learning Performance.
Peng, Q., Ding, Z., Lyu, L., Sun, L., & Chen, C. (2023, August). RAIN: regularization on input and network for black-box domain adaptation. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (pp. 4118-4126).
Pinyoanuntapong, E., Ali, A., Jakkala, K., Wang, P., Lee, M., Peng, Q., ... & Sun, Z. (2023, September). Gaitsada: Self-aligned domain adaptation for mmwave gait recognition. In 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS) (pp. 218-226). IEEE.
Wang, L., Xiao, W., & Ye, S. (2019). Dynamic Multi-label Learning with Multiple New Labels. In Image and Graphics: 10th International Conference, ICIG 2019, Beijing, China, August 23–25, 2019, Proceedings, Part III 10 (pp. 421-431). Springer International Publishing.
Wang, L. (2024). Low-Latency, High-Throughput Load Balancing Algorithms. Journal of Computer Technology and Applied Mathematics, 1(2), 1-9.
Chen, Q., & Wang, L. (2024). Social Response and Management of Cybersecurity Incidents. Academic Journal of Sociology and Management, 2(4), 49-56.
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024). Accelerating Semi-Asynchronous Federated Learning. arXiv preprint arXiv:2402.10991.
Zhao, Y., Wu, J., Qu, P., Zhang, B., & Yan, H. (2024). Assessing User Trust in LLM-based Mental Health Applications: Perceptions of Reliability and Effectiveness. Journal of Computer Technology and Applied Mathematics, 1(2), 19-26.
Xu, C., Qiao, Y., Zhou, Z., Ni, F., & Xiong, J. (2024). Enhancing Convergence in Federated Learning: A Contribution-Aware Asynchronous Approach. Computer Life, 12(1), 1-4.
Wu, R., Zhang, T., & Xu, F. (2024). Cross-Market Arbitrage Strategies Based on Deep Learning. Academic Journal of Sociology and Management, 2(4), 20-26.
Zhou, Z., Xu, C., Qiao, Y., Ni, F., & Xiong, J. (2024). An Analysis of the Application of Machine Learning in Network Security. Journal of Industrial Engineering and Applied Science, 2(2), 5-12.
Wang, X., Qiao, Y., Xiong, J., Zhao, Z., Zhang, N., Feng, M., & Jiang, C. (2024). Advanced network intrusion detection with tabtransformer. Journal of Theory and Practice of Engineering Science, 4(03), 191-198.
Peng, Q., Ding, Z., Lyu, L., Sun, L., & Chen, C. (2022). Toward better target representation for source-free and black-box domain adaptation. arXiv preprint arXiv:2208.10531, 3.
Peng, Q. (2022). Multi-source and Source-Private Cross-Domain Learning for Visual Recognition (Master's thesis, Purdue University).
Zhu, A., Liu, J., Li, K., Dai, S., Hong, B., Zhao, P., & Wei, C. (2024). Exploiting Diffusion Prior for Out-of-Distribution Detection. arXiv preprint arXiv:2406.11105.
Li, K., Xirui, P., Song, J., Hong, B., & Wang, J. (2024). The application of augmented reality (ar) in remote work and education. arXiv preprint arXiv:2404.10579.
Tao, Y. (2023, October). SQBA: sequential query-based blackbox attack. In Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023) (Vol. 12803, pp. 721-729). SPIE.
Tao, Y. (2023, August). Meta Learning Enabled Adversarial Defense. In 2023 IEEE International Conference on Sensors, Electronics and Computer Engineering (ICSECE) (pp. 1326-1330). IEEE.
Tao, Y., Jia, Y., Wang, N., & Wang, H. (2019, July). The fact: Taming latent factor models for explainability with factorization trees. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (pp. 295-304).
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