Multilingual Web Analytics for Tourism Information Recommendation System

抄録

This paper presents a novel approach to tourism information recommendation that incorporates cultural context analysis based on multilingual website access patterns. We analyzed two years of access data from Kagoshima Prefecture's multilingual tourism website across six languages, revealing distinct behavioral characteristics for each language group. Based on these findings, we propose enhancements to our existing eye-tracking-based recommendation system by integrating language-specific weighting in TF-IDF processing and cultural filtering mechanisms. The proposed system aims to provide culturally-appropriate recommendations while encouraging discovery of new tourism experiences.