The growing demand for scientifically grounded and highly personalized fitness plans reveals the huge shortcomings of traditional recommender systems, which cannot overcome template-oriented methods and effectively cope with complex, dynamic user data. As a remedy for this shortcoming, this work utilizes a Large Language Model (LLM) augmented with a domain-specific knowledge graph to develop LLM-SPTRec, a novel framework for intelligent sports training plan generation. This model successfully integrates multi-source heterogeneous user data and enhances the personalization and scientific validity of recommendations by grounding the LLM’s generative process in an expert-elicited Sports Science Knowledge Graph (SSKG). Empirical results on a real-world dataset demonstrate that LLM-SPTRec surpasses traditional baselines—including collaborative filtering, sequential models, and general-purpose LLMs—on fundamental measures of plan coherence, goal relevance, and predicted user satisfaction. The findings of this research provide a new paradigm for the discipline of intelligent health by bridging the gap between big data analysis and expert knowledge in addition to providing a new direction for the overall field of applied AI by demonstrating that knowledge-based LLMs are capable of generating safe, effective, and scientific personal health recommendations.
He et al. (Sat,) studied this question.