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This is a Systematic Review study.

September 10, 2025ElectronicsOpen Access

Large Language Models for Structured and Semi-Structured Data, Recommender Systems and Knowledge Base Engineering: A Survey of Recent Techniques and Architectures

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This systematic review identifies trends in LLM-based recommendations in structured data, indicating ongoing challenges and potential solutions.

LLMs significantly enhance recommendation systems by improving semantic reasoning and personalization, but challenges remain in areas like hallucination mitigation.
The systematic review analyzed 88 studies, revealing key trends including knowledge graphs, domain-specific fine-tuning, and technical innovations like graph-augmented retrieval methods.
Following PRISMA guidelines, the review emphasizes the potential of integrated techniques, highlighting both productivity gains and labor market impacts.
Future directions for LLM-based systems prioritize robustness and adaptation to ensure reliability in real-world scenarios.

Smajić et al. (2025) studied this question.

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This systematic review identifies trends in LLM-based recommendations in structured data, indicating ongoing challenges and potential solutions.