# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 2 Related Work and Contribution
### Contribution  
The most significant novelty of this work lies in its holistic approach to cold-start advertising that simultaneously addresses representation learning, prediction uncertainty, and semantic understanding through a unified framework. The proposed framework fundamentally differs from existing cold-start approaches through its multi-stage architecture that systematically addresses different aspects of the frozen-user problem. Previous literature typically focuses on individual aspects of the cold-start problem, such as either improving embedding methods or leveraging external knowledge, but rarely combines these approaches in a systematic manner. Also, traditional content-based initialization methods rely solely on metadata similarity, while this work combines transformer-enhanced feature representation with ensemble predictions and semantic reranking to create a more robust recommendation pipeline. The incorporation of contextual relevance, recommendation diversification, sentiment-aware ranking, and bias mitigation within the LLM stage represents a comprehensive approach to cold-start challenges that existing single-method solutions cannot achieve.  
Additionally, the components of this framework offer partial scientific contributions as well as potential societal benefits, which can be outlined as follows:  
1. Transformer-Enhanced Feature Extraction: We validate that replacing conventional embedding mechanisms with transformer-based architectures significantly improves feature representation for frozen-start users, yielding superior performance on standard CTR prediction benchmarks. Unlike conventional CTR prediction models that rely on static embeddings and struggle with sparse user data, this approach enables dynamic semantic relationship modeling between user attributes, advertisement content, and contextual information.
2. Ensemble Learning Framework: We develop and validate a novel ensemble approach that effectively combines multiple transformer-enhanced models to produce more accurate CTR predictions than any individual model, addressing the variability inherent in frozen-start scenarios. This ensemble approach differs from traditional cold-start solutions by leveraging diverse modeling perspectives simultaneously rather than relying on individual techniques such as content-based initialization or exploration-driven approaches. The learnable weighted aggregation strategy ensures that model contributions are dynamically balanced based on performance rather than fixed pre-determined weights. This adaptive weighting mechanism addresses the limitation of static ensemble approaches that cannot adjust to varying data distributions in cold-start scenarios.
3. LLM-Powered Reranking and Refinement: This LLM-powered enhancement system addresses critical limitations observed in previous studies by combining the computational efficiency of traditional CTR models with the semantic understanding capabilities of large language models. Unlike existing LLM-based systems that suffer from latency constraints and hallucination issues, this framework employs the LLM only for post-processing the top-five candidates, significantly reducing computational overhead while maintaining the benefits of semantic reasoning. Moreover, conventional approaches that either fully replace traditional models with LLMs or use them in isolation, while the proposed solution leverages the complementary strengths of both paradigms while systematically addressing their individual weaknesses through built-in fairness constraints, sentiment analysis, and real-time adaptation capabilities. This integrated approach not only enhances performance but also provides an ethically grounded solution to persistent cold-start challenges that are frequently neglected in existing systems. Finally, the integration of explainability features and real-time message refinement significantly enhances recommendation transparency and interpretability, enabling dynamic content adaptation that surpasses conventional static recommendation approaches.