A Comprehensive Survey on LLM-Powered Recommender Systems: From Discriminative, Generative to Multi-Modal Paradigms

Large Language Models (LLMs) have become transformative tools in Natural Language Processing (NLP). They are increasingly being integrated into recommendation systems to address existing limitations such as data sparsity, novelty, cold start, and long-tail challenges. Unlike traditional recommendation techniques that rely on user-item interaction matrices, LLMs provide context-aware reasoning and multi-modal processing capabilities. However, existing research mainly focuses on fine-tuning and prompt engineering strategies without fully exploring hybrid models, retrieval-augmented generation (RAG), graph-enhanced recommendations, and evaluation methodologies. This survey offers a comprehensive and structured examination of LLM-based recommendation systems, categorizing them into discriminative, generative, hybrid, graph-enhanced, and multimodal paradigms. Additionally, we explore adaptive fine-tuning techniques, prompt engineering strategies, and retrieval-augmented generation (RAG) approaches that improve LLM performance in personalized recommendations. We also examine evaluation methodologies, including LLM-as-a-Judge frameworks, benchmark limitations, and fairness considerations. Finally, we present a detailed discussion of open challenges, such as hallucination, scalability, bias, and privacy, highlighting critical research gaps and opportunities for future exploration. This survey aims to guide researchers in navigating the evolving landscape of LLM-driven recommendation systems.