X-KGRank: A Knowledge Graph RAG Framework for Explainable Recommendations via Pattern Mining and LLM Re-Ranking
2026-08-03 • Information Retrieval
Information RetrievalArtificial Intelligence
AI summaryⓘ
The authors developed X-KGRank, a recommendation system that combines user behavior data with a knowledge graph to give better movie suggestions and clearer explanations. They built a graph connecting movies, genres, and user ratings from the MovieLens-1M dataset to help the system understand relationships between items. By mixing traditional recommendation methods with language models, their system improved accuracy compared to popular baseline methods and reduced unnecessary explanation generation. They also found that smaller language models perform nearly as well as larger ones in explaining recommendations but tend to make more factual errors.
recommender systemscollaborative filteringlarge language models (LLMs)knowledge graphMovieLens-1M datasetLightGCNSBERTNDCGRecallBPR objective
Authors
Meenakshi Rajpurohit, Jainish Patel
Abstract
Modern recommender systems produce predictions that users cannot interrogate. The two dominant improvements, collaborative filtering and LLM-based reasoning, each fall short: collaborative filtering captures behavioural signals but offers no reasoning, while large language models (LLMs) generate fluent explanations but hallucinate and are poorly grounded in a user's history. We present X-KGRank, a knowledge graph retrieval augmented framework that unifies structural collaborative filtering with LLM-based explanation. From the MovieLens-1M dataset (6,040 users, 3,704 items, 988,129 interactions) we construct a heterogeneous knowledge graph of 9,762 nodes and 999,264 edges spanning three relation types (RATED, HAS_GENRE, and CO_RATED) persisted in Neo4j. We train a LightGCN ranker with content-aware SBERT initialization and a rating weighted BPR objective, and apply a popularity selective routing strategy that grounds long-tail items (1,855 of 3,704) in knowledge-graph paths while serving popular items from pre-trained knowledge, reducing KG-augmented generations by roughly 50%. On the MovieLens-1M test set under a 99-sample protocol, X-KGRank achieves NDCG@10 = 0.2956 and Recall@10 = 0.5371, improving over a strong popularity baseline by 17.1% on both metrics, by 15.6% on NDCG@20 (0.3449 vs. 0.2983), and by 14.6% on MRR (0.2435 vs. 0.2124). Across three LLM backbones evaluated on 16 cases, a 1.5-billion-parameter model (Qwen2.5-1.5B) matches a 7-billion-parameter model (Mistral-7B) on heuristic explanation quality (0.97 vs. 0.94), yet qualitative analysis shows the smaller model is more prone to factual fabrication.