# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 5 Results and Discussion
### Integrated Framework Evaluation Results
The improved performance in Survey 1 suggests that personalized explanations not only increase perceived relevance but may also create a sense of alignment between user expectations and content delivery. Moreover, the study contributes to the Elaboration Likelihood Model (ELM) of persuasion [41]. According to ELM, when users perceive content as personally relevant, they are more likely to process it via the central route, resulting in stronger and more enduring attitude changes. The use of explanation messages in Condition 1 appears to have triggered such central-route processing, leading to higher engagement intentions, including greater willingness to click on ads. Importantly, these findings intersect with emerging work in Explainable AI (XAI). The explanation mechanism offered in Condition 1 can be seen as a practical application of XAI principles [42], which emphasize transparency in algorithmic systems to foster user trust, perceived fairness, and informed decision-making. Results suggest that explanations can act as cognitive bridges, enhancing the perceived integrity and relevance of AI-driven advertising. Taken together, these insights underscore the potential of large language models (LLMs) not only in optimizing ad relevance but also in enhancing presentation quality, message clarity, and user trust through real-time, context-aware explanations. These improvements could have meaningful impacts on user engagement and conversion rates in digital marketing. Further research employing larger and more diverse samples, and a broader range of advertisement types and explanation formats, needs to be conducted. Investigating how explanation length, complexity, and personalization level influence user responses could provide deeper insights. Longitudinal studies could also help determine whether explanation-induced engagement persists over time. Furthermore, incorporating behavioral metrics such as actual click-through rates or dwell time would complement self-reported intentions and offer a more comprehensive understanding of advertising effectiveness. The LLM-powered enhancement module delivers substantial improvements in personalization, explainability, and semantic alignment, but these benefits come with computational costs that require careful system-level optimization. In production deployments, we observed that real-time inference using state-of-the-art models like Gemini 2.5 introduces latency between 200 and 500 ms for typical queries (\(\leq\)512 tokens) when using GPU-accelerated API endpoints. To maintain real-time performance while preservingmodel capabilities, response caching was used to reduce latency for frequent queries. To further reduce latency and computational costs, we plan to investigate:  
* Knowledge distillation--Training a lightweight student model to replace the current ensemble approach, preserving most accuracy while significantly improving inference speed. * Simplified architectures--Evaluating whether slightly less accurate but more efficient models could provide suitable performance for certain use cases [43]. The framework's design explicitly addresses the tension between computational requirements and practical deployment constraints, ensuring suitability for real-world advertising platforms through measurable performance guarantees and cost-efficient resource utilization.