Grounding Techniques in LLM-Based Recommender Systems: A Systematic Literature Mapping

Abstract

The integration of Large Language Models is transforming recommender systems, offering unprecedented capabilities for complex reasoning and natural language generation. However, their propensity to generate hallucinations (incorrect or invented information) compromises reliability and user trust, limiting their adoption in critical domains. Grounding techniques, which link LLM outputs to external, verifiable knowledge sources, are emerging as a fundamental solution, yet the field’s literature remains fragmented. This study aims to conduct systematic literature mapping to characterize and structure knowledge of grounding techniques applied to LLM-based recommender systems. A systematic literature mapping was conducted following the PRISMA protocol. A search of seven academic databases yielded an initial corpus of 1669 documents. After applying inclusion and exclusion criteria, a final set of 57 primary studies was selected and analyzed. The analysis reveals that Retrieval-Augmented Generation is the most widely used grounding technique (29.5%), indicating a clear preference for architectures that decouple LLM reasoning from the knowledge store. A technological bifurcation is observed between proprietary models, such as the GPT family (37.2%), and open-source models, such as LLaMA (24.8%). The application of these systems is expanding from e-commerce to highly critical domains, such as healthcare and finance. Based on the results, a multidimensional taxonomy is proposed to classify grounding techniques by architectural paradigm, data source, underlying mechanism, and intervention phase. The frequent adoption of grounding mechanisms indicates they are transitioning from an optional improvement to a highly prioritized architectural component for LLM-based recommender systems.