## 6 Applications of LLM4 Rec Across Domains
### News and Media Recommendations  
In news and media recommendation, models like T5 excel at distilling lengthy articles into clear, concise summaries, helping readers quickly grasp the main points before diving deeper [26]. Taking this further, RecPrompt introduces a self-adjusting prompting strategy that fine-tunes LLM behavior to better match user preferences, leading to more relevant and engaging news recommendations [140].  
Multimodal systems are also playing a growing role in this space. For instance, MM-Rec leverages both text and image content to better understand user interests, which is particularly valuable for platforms where visuals are central to the experience [141]. In parallel, generative news recommendation (GNR) systems use LLMs to stitch together related articles, offering users a richer, more coherent view of unfolding stories [142].  
Geolocation-aware models are redefining personalization in news recommendation by incorporating users' spatial contexts. Rather than relying solely on explicit location tags, recent advances leverage LLMs to infer implicit geographical cues from content itself. For example, [PERSON] et al. [143] demonstrate how large language models, augmented with knowledge graphs, can uncover local relevance within news articles. Their findings show that such LLM-based systems significantly improve the delivery of personalized, region-specific news--particularly benefiting users like travelers or those seeking local updates. As access to location-rich signals grows, these context-driven recommendations are poised to become a core component of intelligent news delivery systems. Table 8 presents a detailed comparison of all the models discussed.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Base Model** & **Limitations** & **Scalability** & **Latency** & **Multimodal** & **Training Cost/Eval** \\ \hline LLM-KERec [135] & GPT-style LLM + KG & Requires inferential KG construction; cold-start handling needs tuning & High & Moderate & No & Moderate/ HR@10 = 0.678 \\ \hline LLM-PKG [136] & GPT-3 + prod-uct KG & Needs prompt engineering for graph reliability & Moderate & Moderate & Yes & Moderate/ NDCG@10 = 0.652 \\ \hline CRS-LLM [137] & ChatGPT + CRS & Task split complexity in multi-agent flows & High & Moderate & No & Moderate/ F1@Turn = 0.711 \\ \hline ChatGPT-Rank [134] & ChatGPT (API-based) & Latency limits real-time inference & High & Low & No & High/ Recall@20 = 0.689 \\ \hline HybridLMRec [139] & LLM + GBDT/MLP fusion & Requires ensemble tuning across modalities & High & Moderate & Yes & High/Mixed- metric \\ \hline \hline \end{tabular}
\end{table}
Table 7: LLM-Enhanced Retail Recommendation Models.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Model** & **Base Model** & **Limitations** & **Scalability** & **Latency** & **Multimodal** & **Training Cost/Eval** \\ \hline T5 [26] & T5 & High pretraining cost & High & Moderate & No & High/ ROUGE-L = 0.387 \\ \hline RecPrompt [140] & LLM (GPT-style) & Prompt tuning requires extension validation & Moderate & Moderate & No & Moderate/ NDCG@10 = 0.356 \\ \hline \hline \end{tabular}
\end{table}
Table 8: News and media-focused LLM4 Rec Models. Source: Author’s own design.