GIS-Based Personalized Tourism Recommendation Using Association Rule Mining to Support Sustainable Tourism

The increasing availability of tourism information on digital platforms has improved tourists’ access to destination-related data. However, existing tourism information systems often lack effective integration between user preference information and geospatial data, limiting their ability to provide personalized and context-aware recommendations. This study proposes a personalized tourism recommendation system by integrating Geographic Information System (GIS) technology with association rule mining to analyze relationships between user preferences and spatial characteristics of tourist destinations. The proposed system provides map-based visualization, calculates distances between users and destinations, and generates personalized recommendations based on both user interests and spatial proximity. The implementation results demonstrate that the system can generate location-aware and personalized tourism recommendations, supporting users in identifying suitable destinations within their surrounding geographic context. The integration of geospatial processing with association rule mining improves recommendation relevance by incorporating both preference patterns and spatial proximity. Furthermore, the proposed framework has the potential to support more balanced spatial distribution of tourism activities by recommending geographically appropriate destinations rather than concentrating suggestions on highly popular locations. These findings highlight the value of combining geospatial technologies with data mining techniques to support tourism recommendation systems and spatially informed tourism planning.