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GraphRAG-IRL is introduced, a hybrid recommendation framework leveraging graph-grounded feature construction, inverse reinforcement learning (IRL), and persona-guided LLM re-ranking to address limitations of LLMs as standalone recommenders. A heterogeneous knowledge graph is constructed to retrieve individual and community preference context, which is then used to train a Maximum Entropy IRL model for calibrated pre-ranking, followed by LLM re-ranking on a short candidate list. Experiments on MovieLens and KuaiRand demonstrate that GraphRAG-IRL significantly improves NDCG@10 compared to supervised baselines and outperforms IRL-only baselines with persona-guided LLM fusion.
LLMs are unreliable ranking engines on their own, but fusing them with graph-grounded IRL creates a recommender system that's more than the sum of its parts, boosting NDCG@10 by up to 16.8%.
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.
