# A Novel Framework Leveraging Large Language Models to Enhance Cold-Start Advertising Systems
## 2 Related Work and Contribution
### LLM-Based Systems  
Recent advances in large language models (LLMs) offer a new avenue for addressing cold-start challenges. With their ability to perform zero-shot and few-shot reasoning, LLMs can infer meaningful representations from textual descriptions alone, eliminating the need for historical interaction data. Prompted GPT-3 has been shown to rank items based on natural language preference statements, achieving accuracy comparable to collaborative filtering in near cold-start environments [22]. Similarly, LLMTreeRec recasts item recommendation as a language-modeling task, using large-scale LLMs like GPT-4 to cluster and rank items without domain-specific training [23]. However, these general-purpose models may struggle with long-tail coverage or niche inventory due to their broad pretraining corpus.  
To address scalability and efficiency constraints in production environments, Alibaba's FilterLLM introduced a distilled LLM that generates engagement-probability vectors directly from ad text. This approach supports billion-scale inventory filtering with a 30\(\times\) efficiency gain and a measurable CTR lift in live deployments [24]. Google's Gemini 2.0, deployed via Vertex AI, extends LLM capabilities further into multimodal reasoning by integrating text, image, and audio data to infer creative relevance and audience fit, which is especially valuable for cold-start ad selection and reranking [25]. However, domain adaptation remains critical. For example, LawLLM, a domain-specific model tailored for the U.S. legal domain, that significantly improves zero- and few-shot performance through task customization and retrieval-enhanced architecture [26]. This demonstrates the value of domain specialization for validating models such as Gemini-based rerankers in real-world applications.  
The integration of LLMs into explainable and interpretable recommendation systems is gaining traction. Applied language models for health prediction tasks, emphasizing explainability and demonstrating the utility of NLP-derived features for interpretable, domain-specific prediction across verticals like healthcare and nutrition [27]. These findings are highly relevant for advertising use cases that require transparent justifications for recommendations or decisions.  
Moreover, [PERSON] et al. (2025) present a comprehensive survey of cold-start recommendation approaches leveraging LLMs, encompassing prompt-based, generative, and hybrid architectures [28]. Their framework situates current efforts within a broader methodological landscape and underscores the importance of adapting LLM architectures to domain-specific, high-precision use cases. These insights reinforce the trajectory of hybrid LLM pipelines, where task-specific distilled models handle real-time scoring, while candidate generation is augmented through retrieval or caching mechanisms. Nonetheless,cution is warranted as LLMs may hallucinate associations not supported by empirical data, necessitating calibration through real-time feedback loops and brand-safety constraints.  
LLM-based systems face three key barriers to production adoption: (1) prohibitive computational costs and latency that conflict with real-time ad serving requirements, (2) risks of hallucinated recommendations requiring extensive safety guardrails and calibration, and (3) domain adaptation challenges where general pretraining fails to capture advertising-specific nuances. Ongoing research focuses on optimizing LLMs via distillation, quantization, and modular integration--aligning their expressiveness with production-level latency demands. These developments suggest a future in which LLMs enhance traditional CTR prediction pipelines with world knowledge and robust generalization, especially under data-sparse or cold-start conditions.