Large Language Models for Recommender Systems: A Problem-Driven Survey

With the increasing demand for personalization in modern digital ecosystems, Recommender Systems (RS) have become essential for delivering user-centric services. However, traditional RS approaches, which are primarily driven by algorithmic optimization over historical interaction data, still face well-known challenges such as the cold-start problem, limited interpretability, and insufficient personalization quality. Meanwhile, the rapid evolution of Large Language Models (LLMs) has showcased remarkable capabilities in semantic understanding, reasoning, and generalization, presenting a promising opportunity to rethink the design of recommendation paradigms. A central research question is how to systematically leverage LLMs to address fundamental RS challenges while avoiding unnecessary complexity, inefficiency, or unintended consequences. In this paper, we provide the first problem-driven synthesis of LLM-empowered RS. We propose a comprehensive taxonomy that organizes existing integration efforts into four major problem domains: Cold-start problem, poor interpretability, suboptimal user experience, and novel challenges introduced by LLMs themselves (e.g., hallucination, bias, and inefficiency). We begin by formally characterizing each problem domain and reviewing its significance in recommendation research. We then categorize recent advances by methodological perspective. Finally, we offer insights and future directions for building adaptive, efficient, and value-aligned RS enhanced by LLM capabilities.