Sustainable Recommender Systems for Tourism

In this episode, we speak with Ashmi Banerjee, a doctoral candidate at the Technical University of Munich, about her pioneering research on AI-powered recommender systems in tourism. Ashmi illuminates how these systems can address exposure bias while promoting more sustainable tourism practices through innovative approaches to data acquisition and algorithm design. Key highlights include leveraging large language models for synthetic data generation, developing recommendation architectures that balance user satisfaction with environmental concerns, and creating frameworks that distribute tourism more equitably across destinations. Ashmi's insights offer valuable perspectives for both AI researchers and tourism industry professionals seeking to implement more responsible recommendation technologies.

Guest

Ashmi Banerjee: Ashmi is currently a doctoral researcher at the Chair of Connected Mobility at the Technical University of Munich. Her research focuses on Recommender Systems, specifically in the tourism domain. She graduated with a master's degree in Computer Science in 2019 from the same university and also holds three years of industry experience at different companies across Germany. She is passionate about using technology to automate tedious tasks and is always excited to tackle new technical challenges. Over the past two years, she has delivered over 50 tech talks across 15+ countries on three continents. She was named one of the 100 technologists to watch for 2023 and won the Google Developer Expert Community Award (Rising Star), the 2023 Women Who Code Applaud Her Award (Data Science), and the DevelopHER Awards 2022 (Emerging Talent). As a Google Women Techmakers (WTM) Ambassador and diversity advocate, she is dedicated to closing the gender gap in STEM through her involvement in various women in STEM networks. When not sitting in front of her computer, she can usually be found traveling, collecting passport stamps, and fridge magnets. ✈️