A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges

• URL: http://arxiv.org/abs/2507.21117v1
• Date: Thu, 17 Jul 2025 06:03:57 GMT
• Title: A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
• Authors: Rahul Raja, Anshaj Vats, Arpita Vats, Anirban Majumder,
• Abstract summary: Large Language Models (LLMs) can be leveraged to tackle key challenges in recommender systems.<n>LLMs enhance personalization, semantic alignment, and interpretability without requiring extensive task-specific supervision.<n>LLMs enable zero- and few-shot reasoning, allowing systems to operate effectively in cold-start and long-tail scenarios.
• Score: 5.436611859202691
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems have traditionally followed modular architectures comprising candidate generation, multi-stage ranking, and re-ranking, each trained separately with supervised objectives and hand-engineered features. While effective in many domains, such systems face persistent challenges including sparse and noisy interaction data, cold-start problems, limited personalization depth, and inadequate semantic understanding of user and item content. The recent emergence of Large Language Models (LLMs) offers a new paradigm for addressing these limitations through unified, language-native mechanisms that can generalize across tasks, domains, and modalities. In this paper, we present a comprehensive technical survey of how LLMs can be leveraged to tackle key challenges in modern recommender systems. We examine the use of LLMs for prompt-driven candidate retrieval, language-native ranking, retrieval-augmented generation (RAG), and conversational recommendation, illustrating how these approaches enhance personalization, semantic alignment, and interpretability without requiring extensive task-specific supervision. LLMs further enable zero- and few-shot reasoning, allowing systems to operate effectively in cold-start and long-tail scenarios by leveraging external knowledge and contextual cues. We categorize these emerging LLM-driven architectures and analyze their effectiveness in mitigating core bottlenecks of conventional pipelines. In doing so, we provide a structured framework for understanding the design space of LLM-enhanced recommenders, and outline the trade-offs between accuracy, scalability, and real-time performance. Our goal is to demonstrate that LLMs are not merely auxiliary components but foundational enablers for building more adaptive, semantically rich, and user-centric recommender systems

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