Explainable Attraction Recommendations Integrating Knowledge Graph and Collaborative Filtering: A Case Study in Henan Province, China

ABSTRACT

Explainable attraction recommendations play a crucial role in enhancing user decision confidence and advancing smart tourism development. In response to the two major challenges in the existing attraction recommendation methods, namely the difficulty in balancing interpretability and recommendation accuracy as well as the lack of transparency in the weight allocation of multi‐source heterogeneous information, a method for explainable attraction recommendation that integrates knowledge graph and collaborative filtering is proposed. First, we designed an attraction recommendation metric by comprehensively considering both fundamental attraction characteristics and collective intelligence. Second, we developed an explainable attraction recommendation algorithm that integrates knowledge graph and collaborative filtering. Finally, using attractions in Henan Province, China, as our research area, we conducted accuracy analysis of the recommendation method and designed case studies based on diverse user needs. Results demonstrate that our method achieves 93.8% accuracy, enabling intelligent and effective attraction recommendations while ensuring the explainability of the results.