GraphRAG-IRL: Personalized Recommendation with Graph-Grounded Inverse Reinforcement Learning and LLM Re-ranking

Original Abstract

Personalized recommendation requires models that capture sequential user preferences while remaining robust to sparse feedback and semantic ambiguity. Recent work has explored large language models (LLMs) as recommenders and re-rankers, but pure prompt-based ranking often suffers from poor calibration, sensitivity to candidate ordering, and popularity bias. These limitations make LLMs useful semantic reasoners, but unreliable as standalone ranking engines. We present \textbf{GraphRAG-IRL}, a hybrid recommendation framework that combines graph-grounded feature construction, inverse reinforcement learning (IRL), and persona-guided LLM re-ranking. Our method constructs a heterogeneous knowledge graph over items, categories, and concepts, retrieves both individual and community preference context, and uses these signals to train a Maximum Entropy IRL model for calibrated pre-ranking. An LLM is then applied only to a short candidate list, where persona-guided prompts provide complementary semantic judgments that are fused with IRL rankings. Experiments show that GraphRAG-IRL is a strong standalone recommender: IRL-MLP with GraphRAG improves NDCG@10 by 15.7\% on MovieLens and 16.6\% on KuaiRand over supervised baselines. The results also show that IRL and GraphRAG are superadditive, with the combined gain exceeding the sum of their individual improvements. Persona-guided LLM fusion further improves ranking quality, yielding up to 16.8\% NDCG@10 improvement over the IRL-only baseline on MovieLens ml-1m, while score fusion on KuaiRand provides consistent gains of 4--6\% across LLM providers.