Personalized Graph-Based Retrieval for Large Language Models
Authors: name: Steven Au, name: Cameron J. Dimacali, name: Ojasmitha Pedirappagari, name: Namyong Park, name: Franck Dernoncourt, name: Yu Wang, name: Nikos Kanakaris, name: Hanieh Deilamsalehy, name: Ryan A. Rossi, name: Nesreen K. Ahmed

As large language models (LLMs) evolve, their ability to deliver personalized
and context-aware responses offers transformative potential for improving user
experiences. Existing personalization approaches, however, often rely solely on
user history to augment the prompt, limiting their effectiveness in generating
tailored outputs, especially in cold-start scenarios with sparse data. To
address these limitations, we propose Personalized Graph-based
Retrieval-Augmented Generation (PGraphRAG), a framework that leverages
user-centric knowledge graphs to enrich personalization. By directly
integrating structured user knowledge into the retrieval process and augmenting
prompts with user-relevant context, PGraphRAG enhances contextual understanding
and output quality. We also introduce the Personalized Graph-based Benchmark
for Text Generation, designed to evaluate personalized text generation tasks in
real-world settings where user history is sparse or unavailable. Experimental
results show that PGraphRAG significantly outperforms state-of-the-art
personalization methods across diverse tasks, demonstrating the unique
advantages of graph-based retrieval for personalization.