The emergence of large generative models is transforming the landscape of recommender systems. One of the most fundamental components in building these models is action tokenization, the process of converting human-readable data (e.g., user-item interactions) into machine-readable formats (e.g., discrete token sequences). In this tutorial, we present a comprehensive overview of existing action tokenization techniques, converting actions to (1) item IDs, (2) textual descriptions, and (3) semantic IDs, and explore how they relate to the development of large generative recommendation models. We then make an in-depth discussion on the challenges, open questions, and potential future directions from the perspective of action tokenization, aiming to inspire the design of next-generation recommender systems.
