Optimized unsupervised semantic trajectory mining for personalized tourism recommendations

Key Points

• User behavior is better predicted with optimized semantic trajectory mining algorithms, leading to tailored tourist experiences and increased satisfaction.
• Findings reveal a substantial increase of 25% in the accuracy of personalized recommendations using the proposed methodology over traditional methods.
• Assessment of unsupervised learning techniques on tourism data highlights the effectiveness of algorithmic adaptations in real-world settings.
• Applications of this approach may significantly enhance user interaction with tourism platforms and services, promoting destination exploration.