Sustainable Recommender Systems for Tourism

Summary

The post interviews doctoral candidate Ashmi Banerjee about her research on AI-driven recommender systems that mitigate exposure bias and promote sustainable tourism. She describes using large language models to generate synthetic data, designing recommendation architectures that balance user satisfaction with environmental impact, and creating frameworks that more evenly distribute tourist flows across destinations. The discussion highlights practical strategies for AI researchers and tourism professionals aiming to adopt responsible recommendation technologies.