# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 6 Conclusions  
This manuscript presents a novel framework that addresses the frozen-user problem in personalized advertising through a holistic, three-stage architecture integrating transformer-enhanced feature extraction, ensemble modeling, and LLM-powered semantic refinement. This innovative approach bridges the gap between traditional CTR prediction and advanced semantic understanding, delivering superior accuracy, transparency, and user trust in cold-start scenarios. Key contributions include (1) a transformer-driven feature representation that dynamically models semantic relationships in sparse data, surpassing conventional embeddings; (2) a robust ensemble methodology with learnable weighted aggregation, enhancing prediction stability over single-model approaches; and (3) an LLM-powered stage that ensures contextual relevance, recommendation diversification, sentiment alignment, and fairness while generating transparent explanations. Unlike existing methods that focus narrowly on embedding improvements or external knowledge, our framework systematically tackles the limitations of feature sparsity, computational overhead, and lack of interpretability. Empirical validation on the DigiX and Avazu datasets shows significant AUC and accuracy gains, while a user study confirms enhanced ad relevance, message clarity, and trust. The work advances the state of the art by demonstrating how LLMs can be effectively integrated into traditional recommendation pipelines to address the frozen-user problem while simultaneously improving user experience through enhanced transparency and personalization. The multi-faceted application of LLMs for contextual relevance, diversification, sentiment awareness, and bias mitigation shows particular promise for future research directions. The manuscript represents a significant advancement over several state-of-the-art approaches:  
1. Versus Traditional CTR Models: Compared to classic approaches this framework adds semantic understanding and explainability that addresses fundamental limitations in these models. 2. Versus Pure LLM Approaches: Unlike recent work that uses LLMs as end-to-end recommendation engines, this hybrid approach leverages the strengths of both statistical modeling and natural language processing while mitigating their respective weaknesses. 3. Versus Other Cold-Start Solutions: The framework offers advantages over traditional cold-start techniques such as meta-learning and content-based filtering by incorporating dynamic contextual understanding and transparent explanations. 4. Versus Explainable Recommendation Systems: While explainable recommendation systems have gained attention, this framework goes beyond post hoc explanations by integrating explanation generation directly into the recommendation process. On the other hand, the framework presents opportunities for optimization in production environments, particularly regarding the computational efficiency of ensemble models and Gemini 2.5 integration in latency-critical applications. In future work, we plan to enhance the framework's energy efficiency by exploring knowledge distillation techniques to train lightweight student models that retain the ensemble's accuracy with reduced computational overhead. Additionally, we will investigate spiking neural networks, which offer inherent energy efficiency for CTR prediction, as demonstrated in prior work [42]. To address latency concerns with Gemini 2.5, we plan to incorporate lighter and faster LLMs (e.g., DistilBERT), which may provide comparable semantic reranking and personalization capabilities with lower resource demands. Furthermore, we will conduct more extensive user studies with larger, diverse samples across varied ad contexts to validate explanation quality and assess further the longitudinal effects on user engagement. These studies will incorporate real-world behavioral metrics, such as click-through rates, to provide a comprehensive evaluation of the framework's practical impact in production environments. Conceptualization, I.V.