Sport tourism has experienced significant growth within the tourism industry, driven by the increasing demand of special interest tourists to watch or participate in sports events with local sightseeing. However, the massive volume of available information related to sport events may cause challenges to existing recommendation systems, which struggle to provide tailored suggestions for these niche tourists. Therefore, this paper proposes a novel, context-aware recommender framework that utilises the ontology-driven approach with unsupervised machine learning techniques to deliver personalised event matches for running tourists. Using an ontology-driven approach, the framework establishes a knowledge base of user profiles and running events. Furthermore, K-modes clustering was also applied to categorise participants based on their event participation characteristics, while the Apriori algorithm was used to uncover hidden relationships influencing event selection. To ensure the statistical integrity of the discovered association rule, permutation testing was implemented to mitigate bias inherent in small sample sizes. By integrating refined association rules with Jena rules, the resulting prototype offers adaptive, personalised, and contextually relevant running event recommendations that evolve with shifting user preferences and trends. The effectiveness of the prototype is confirmed through rigorous validation and evaluation across various sport tourism scenarios.