ID Crisis, Resolved: When Semantic IDs Stop Fighting Hash IDs
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. fileciteturn0file0 ...