Dipartimento di Ingegneria informatica, automatica e gestionale

This talk explores the emerging field of generative recommender systems and their foundation in large language models. We'll examine what foundation models are, when they offer advantages for recommendation tasks, and when traditional approaches may be more suitable.

A key focus will be on Semantic IDs—a novel approach to representing items in ways that LLMs can understand and generate. I'll demonstrate how to leverage Semantic IDs to build LLM-based recommender systems, covering practical aspects including:

• Implementing models for Semantic ID generation
• Fine-tuning large language models for recommendation tasks
• Optimization strategies for efficient training
• Evaluation methodologies for generative recommendation systems

The session will provide both conceptual foundations and practical insights for researchers and practitioners interested in applying generative AI to recommendation problems.

Bio: Kim Falk is a computer scientist turned Recommender Scientist, and spent more than the last decade working on recommender systems. Kim has worked in the industry since 2003 and has been active in the research community, publishing papers and serving as a chair at the ACM RecSys conference for 5 years. Kim is the author of Practical Recommender Systems.