Join us for an engaging session on building a Retrieval-Augmented Generation (RAG) system, emphasizing generating sustainable travel recommendations.

Our discussion will center around leveraging Google's Gemini model via Vertex AI to generate text, augmenting this process with relevant content from a vector database populated with Wikivoyage data for 160 European cities.

In addition to enhancing the travel experience through personalized suggestions, we'll introduce Sustainability Augmented Reranking (SAR), a novel approach to integrating sustainability into traditional tourism recommender systems (TRS). This modification incorporates a city's popularity and seasonal demand into the prompt augmentation process, aligning recommendations with sustainability goals. By doing so, the SAR-enhanced RAG system delivers more responsible and eco-conscious travel suggestions, balancing visitor preferences with environmental impact.

Additionally, we'll discover how to deploy our application effortlessly on Gradio—an open-source Python package that simplifies building demos or web applications without requiring any JavaScript, CSS, or web hosting expertise.

The talk will be based on the following papers and the demo:

• https://arxiv.org/abs/2409.18003 (accepted for publication at RecSoGood 2024 Workshop, co-located with the ACM RecSys 2024 conference).

• https://link.springer.com/article/10.1007/s40558-024-00303-1 (published in Springer Journal of Information Technology & Tourism, 2025)

• https://huggingface.co/spaces/ashmib/green-city-finder (Google AI Sprint 2024 submission by Ashmi Banerjee)