LLMAR: A Tuning-Free Recommendation Framework for Sparse and Text-Rich Industrial Domains

Excerpt

arXiv:2604.16379v3 Announce Type: replace-cross Abstract: Industrial B2B applications (e.g., construction site risk prediction, material procurement) face extreme data sparsity yet feature rich textual interactions. In such environments, traditional ID-based collaborative filtering fails lacking co-occurrence signals, while fine-tuning standard Large Language Models (LLMs) incurs high operational costs and struggles with frequent data drift. We propose LLMAR (LLM-Annotated Recommendation), a tun