# Personalized Risks and Regulatory Strategies of Large Language Models in Digital Advertising

arXiv: https://arxiv.org/abs/2505.04665 · HTML: https://mlsanity.com/paper/2505.04665/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2025.

## Built on by (most cited first, 8 of 8)

- [LLM-Driven E-Commerce Marketing Content Optimization: Balancing Creativity and Conversion](https://mlsanity.com/paper/2505.23809/) (2025)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [Research on Model Parallelism and Data Parallelism Optimization Methods in Large Language Model-Based Recommendation Systems](https://mlsanity.com/paper/2506.17551/) (2025)
- [Meta-Learning for Cold-Start Personalization in Prompt-Tuned LLMs](https://mlsanity.com/paper/2507.16672/) (2025)
- [Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems](https://mlsanity.com/paper/2506.11421/) (2025)
- [Human-AI Co-Creation: A Framework for Collaborative Design in Intelligent Systems](https://mlsanity.com/paper/2507.17774/) (2025)
- [Multimodal Health Risk Prediction System for Chronic Diseases via Vision-Language Fusion and Large Language Models](https://mlsanity.com/paper/2509.18221/) (2025)
- [Analyzing the Impact of Credit Card Fraud on Economic Fluctuations of American Households Using an Adaptive Neuro-Fuzzy Inference System](https://mlsanity.com/paper/2509.19363/) (2025)

## Abstract (arXiv)

Although large language models have demonstrated the potential for personalized advertising recommendations in experimental environments, in actual operations, how advertising recommendation systems can be combined with measures such as user privacy protection and data security is still an area worthy of in-depth discussion. To this end, this paper studies the personalized risks and regulatory strategies of large language models in digital advertising. This study first outlines the principles of Large Language Model (LLM), especially the self-attention mechanism based on the Transformer architecture, and how to enable the model to understand and generate natural language text. Then, the BERT (Bidirectional Encoder Representations from Transformers) model and the attention mechanism are combined to construct an algorithmic model for personalized advertising recommendations and user factor risk protection. The specific steps include: data collection and preprocessing, feature selection and construction, using large language models such as BERT for advertising semantic embedding, and ad recommendations based on user portraits. Then, local model training and data encryption are used to ensure the security of user privacy and avoid the leakage of personal data. This paper designs an experiment for personalized advertising recommendation based on a large language model of BERT and verifies it with real user data. The experimental results show that BERT-based advertising push can effectively improve the click-through rate and conversion rate of advertisements. At the same time, through local model training and privacy protection mechanisms, the risk of user privacy leakage can be reduced to a certain extent.