Enhancing Embedding Representation Stability in Recommendation Systems with Semantic ID

The exponential growth of online content has posed significant challenges to ID-based models in industrial recommendation systems, ranging from extremely high cardinality and dynamically growing ID space, to highly-skewed engagement distributions, to prediction instability as a result of natural id life cycles. This paper examines these challenges and introduces Semantic ID prefix-ngram, a novel token parameterization technique that significantly improves the performance of the original Semantic ID. Semantic ID prefix-ngram creates semantically meaningful collisions by hierarchically clustering items based on their content embeddings, as opposed to random assignments. Through extensive experimentation, we demonstrate that Semantic ID prefix-ngram not only addresses embedding instability but also significantly improves tail id modeling, and mitigates representation shifts. We report our experience of integrating Semantic ID into Meta’s production Ads Ranking system, leading to notable performance gains.