IDs That Mean Something: A Hands-On Tutorial on Semantic IDs for Recommender Systems
Roger Zhe Li, Aghiles Salah, Mete Sertkan, Changhong Jin, Yingjie Niu, Ruihai Dong, Barry Smyth
Traditional recommender systems rely on arbitrary integer IDs to identify items, which carry no semantic information and force models to learn representations from interaction data. While leveraging content information can partially mitigate cold-start, this paradigm inherently suffers from exploding embedding tables and scenario-specific architectures. Recent years have seen the emergence of semantic IDs, which encode semantic information directly into the item identity, reflecting content, hierarchy, and relationships. This framework addresses the above limitations by offering a principled way to encode large-scale semantic information into item identity, benefiting both generative and discriminative applications and unlocking more native unification across modalities, domains, and tasks. This tutorial traces the evolution from raw IDs to semantic IDs as a scalable means of information utilization, offering a comprehensive review of their development and applications across multi-modality, cold-start, and cross-domain recommendation. Hands-on exercises guide attendees through building and evaluating semantic IDs, training models, and exploring cross-domain transfer. We conclude by discussing open challenges, including dynamic semantic IDs and multi-stakeholder utility. All materials of the tutorial could be found at the official website https://semantic-id-tutorial.github.io.
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