Semantic Ids

Opening — Why this matters now Generative Sequential Recommendation (GSR) is having its moment. By reframing recommendation as an autoregressive generation problem over Semantic IDs (SIDs), the field promises something long overdue: a unified retrieval-and-ranking pipeline that actually understands what items mean, not just where they sit in an embedding table. But beneath the hype sits an uncomfortable truth. Most lightweight GSR systems are quietly sabotaging themselves. They collapse their own codebooks, blur semantic boundaries, and then wonder why performance tanks—especially on sparse, long‑tail data. PRISM arrives as a sober correction to that pattern. ...

Opening — Why this matters now Recommender systems have quietly hit an identity crisis. As item catalogs explode and user attention fragments, sequential recommendation models are being asked to do two incompatible things at once: memorize popular items with surgical precision and generalize intelligently to the long tail. Hash IDs do the former well. Semantic embeddings do the latter—sometimes too well. The paper “The Best of the Two Worlds: Harmonizing Semantic and Hash IDs for Sequential Recommendation” formalizes why these worlds keep colliding, and proposes a framework—H2Rec—that finally stops forcing us to choose sides. fileciteturn0file0 ...